Recently, the marriage of Deep Learninggoodfellow2016deep and Reinforcement Learning (RL) sutton1998introduction leads to significant breakthroughs in machine based decision making systems, especially for computer games. Systems based on deep reinforcement learning (DRL), trained either from scratch or from a pre-trained model, can take inputs of raw observation features and achieve impressive performance in a wide range of applications, including playing the board game GO silver2016mastering ; silver2017mastering , playing video games (e.g., Atari mnih2015human , the first person shooting game Doom/ViZDoom kempka2016vizdoom ; wu2017 or Quake/DeepmindLab beattie2016deepmind , Dota 2 openai-five ), Robot Visuomotor Control levine2016end , Robot Navigation zhu2016target ; sadeghi2016cad , etc. The learned policy/controller can work surprisingly well, and in many cases even achieves super-human performance mnih2015human ; silver2017mastering .
However, Starcraft II (SC2) sc2le , which is widely considered as the most challenging RTS game, still remains unsolved. In SC2, a human player has to manipulate tens to hundreds of units222In the 1 vs 1 game, the maximal number of moving units controlled by a player could exceed a hundred. for multiple purposes, e.g., collecting two types of resources, expanding for extra resources, upgrading technologies, building other units, sending squads for attacking or defending, performing micro managements over each unit for a battle, etc. This is one important factor why SC2 is more challenging than Dota 2, in which the total number of units needed to be controlled is up to five (manipulated by five players respectively). Figure 1 shows a screenshot of what human players work with. The units of the opponent are hidden to the player, unless they are in the viewing range of the units controlled by the player. The player needs to send scouting units to spy on the opponent’s strategy. All decisions must be made in real time. In terms of designing AI agents, SC2 involves a large observation space, a huge action space, partial observations, simultaneous move for all players, and long horizon delayed rewards for local decisions. All of these factors make SC2 extremely challenging. To push the frontier of AI research, Deepmind and Blizzard jointly developed the StarCraft II Learning Environment (SC2LE) sc2le . Some recent results by Deepmind sc2le ; zambaldi2018relational showed that their AI agent can achieve the performance level of professional players in a few mini games, in which the agent, for example, manipulates a Marine to reach a beacon (MoveToBeacon) or manipulates several Marines to defeat several Roaches (DefeatRoaches), etc. However, for full games, the current AI agents are still far from achieving human professional level performance.
This paper investigates AI agents for full games, and for simplicity, we restrict our study to the following setting: 1vs1 Zerg-vs-Zerg on the AbyssalReef map. We develop two AI agents — the AI agent TStarBot1 is based on deep reinforcement learning over flat actions and the AI agent TStarBot2 is based on rule controllers over hierarchical actions. Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game, noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting. It is also worth mentioning that according to some informal discussions from the StarCraft II forum, level 10 built-in AI is estimated to be Platinum to Diamond scii-forum , which are equivalent to top 50% - 30% human players in the ranking system of Battle.net Leagues liquid .
The AI agent TStarBot1 is based on “flat” action modeling, which employs a flat action structure and produces a number of discrete actions. This design makes it immediately ready for any off-the-shelf RL algorithm that takes discrete actions as input. The AI agent TStarBot2 is based on “deep” action modeling, which employs a manually specified action hierarchy. Intuitively, “deep” modeling can better capture the action dependencies. However, the training becomes more challenging since learning methods for the complex hierarchical RL will be needed. To avoid the difficulty, we simply adopt a rule based controller for TStarBot2 in this work.
To the best of our knowledge, this is the first public work to investigate the AI agents that can defeat the built-in AIs in a Starcraft II full game. The code will be open sourced tstarbots
. We hope the proposed framework will be beneficial for future research in several possible ways: 1) Be a baseline for a hybrid system, in which more and more learning modules can be included while rules are still utilized to express logic that are hard to learn; 2) Generate trajectories for imitation learning; and 3) Be an opponent for self-play training.
2 Related Work
The RTS game StarCraft I has been used as a platform for AI research for many years. Please refer to ontanon2013survey ; churchill2016heuristic for a review. However, most research considers searching algorithms or multi-agent algorithms that cannot be directly applied to the full game. For example, many multi-agent reinforcement learning algorithms have been proposed to learn agents either independently tan1993multi or jointly foerster2017counterfactual ; usunier2016episodic ; peng2017multiagent ; sukhbaatar2016learning with communications to perform collaborative tasks, where a StarCraft unit (e.g., a Marine, a Zealot, a Zergling, etc.) is treated as an agent. These methods can only handle mini-games, which should be viewed as snippets of a full game.
, but the reported performance was relatively poor. Recently, relational neural network is proposed for playing the SC2 gamezambaldi2018relational using a player-level modeling. However, the studies were conducted on mini-games instead of full games.
Historically, some rule based decision systems have been successful in specific domains, e.g., MYCIN for medical diagnosis or DENTRAL for molecule discovery russell2016artificial . The only way they can be improved is by manually adding knowledge, which makes them lacking the ability to learn from data or from interacting with the environment.
Rule based AI-bot is popular in the video game industry. However, the focus there is to develop tools for code reuse (e.g., Finite State Machine or Behavior Tree millington2009artificial
), not on how the rules can be combined with learning based methods. There exists recent work that tries to perform reinforcement learning or evolutionary algorithms over Behavior Treesmarzinotto2014towards ; perez2011evolving . However, the observations are tabular, and are unrealistic for large scale games such as SC2.
Our macro action based agent (Section 3.2) is similar to that of tian2017elf , where the authors adopted macro actions for a customized mini RTS game. However, our macro action set is much larger. It encodes concrete rules for the execution, and is therefore more realistic for SC2LE.
In Section 3.3, we describe our implementation of the hierarchical action based agent. Our approach is inspired by the modular design of UAlbertaBot churchill2016heuristic , which is also widely adopted in the literature of StarCraft I. In spirit, the hierarchical action set is similar to the FeUdal network vezhnevets2017feudal , but we do not pursue an end to end learning of the complete hierarchy. We also allow each action to have its own observation and policy. This helps the system to rule out noisy information, as discussed in van2017hybrid .
3 The Proposed TStarBot Agents
Among the multiple challenges of SC2, this work focuses on how to deal with the huge action space, which, we argue, arises from the game’s complex intrinsic structure. Specifically, there are several aspects.
Hierarchical nature. In RTS games, the long-horizon decision process requires complex hierarchical actions. A human player often summarizes his or her thinking in several abstraction levels including global strategies, local tactics, and micro executions. If a learning algorithm is unaware of the higher “abstraction levels” (i.e., the action hierarchy) and works directly on the the huge number of basic atomic actions in full game play, then it is inevitably difficult for RL training, especially due to inefficient exploration. For example, PySC2 sc2le defines the action space over the low-level human user interface, involving hundreds of hot-keys and thousands of mouse-clicks over screen coordinates. Following this setting, even the state-of-the-art RL algorithm can only achieve success in playing toy mini-games that has much shorter horizon than the full game zambaldi2018relational . Although many papers have been devoted to automatically learning meaningful hierarchies in a Markovian Decision Process sutton1999between ; bacon2017option ; ghazanfari2017autonomous ; vezhnevets2016strategic ; vezhnevets2017feudal ; frans2017meta , none of these methods can work efficiently on environments as complex as SC2. Therefore, it is a challenging task to utilize the hierarchical nature of the decision process, and define a tractable decision space with manageable exploration efforts.
Hard-rules in SC2 are difficult to learn. Another challenge of learning-based agent design is the large number of “hard rules” in RTS game. These hard rules may be considered as the “laws of physics” that cannot be violated. They are easily interpreted by human players through in-game textual instructions, but are difficult for learning algorithms to discover using a pure trial-and-error approach. Consider a human player who starts to play StarCraft-II, he or she can easily learn from a textual tutorial to first select a drone unit to build a RoachWarren unit before selecting a larva unit to produce a Roach unit. By doing so, he or she can quickly discover the following hard game rules:
RoachWarren is a prerequisite of producing a Roach.
RoachWarren is built by a Drone.
Roach is produced from a Larva.
Producing a Roach requires 75 minerals and 25 gas.
In SC2 there are thousands of such dependencies, constituting a technology dependency tree, abbreviated as TechTree (See also Section 3.1). TechTree serves as the most important prior knowledge that a human player should learn from textual tutorials or materials on the game interface, other than exploration through trial-and-error. A learning algorithm unaware of the TechTree must spend a huge amount of time to learn the hard games rules, which introduces extra difficulty, especially when the feedback signal is sparse and delayed (i.e., the win/loss reward received at the end of each game). Thus, in RTS games, it is important to think about how to design a mechanism that can encode these hard game rules directly into the agent’s prior knowledge, instead of relying on pure learning.
Uneconomical learning for trivial decision factors. It is also worth noting that despite the tremendous decision space of SC2, not all decisions matter. In other words, a considerable amount of decisions are redundant in that they will have negligible effects on the game’s final outcome. For instance, when a human player wants to build a RoachWarren during the game, there are at least three decision factors he or she has to consider:
Decision Factor 1: When to build it? (Non-trivial)
Decision Factor 2: Which Drone builds it? (Trivial)
Decision Factor 3: Where to build it? (Trivial)
A proficient player would conclude that: 1) the first decision factor is a non-trivial one since when to build a RoachWarren will have a considerable impact on the entire game progress; 2) the second decision is trivial because any random Drone can do the work with negligible difference of building efficiency; and 3) the third factor can also be taken as trivial as long as the target position is not too far away from the self-base and the geometry defense is not considered. Learning algorithms unaware of the factors may spend significant efforts on filtering out trivial decisions. For example, an accurate placement decision of “where to build” requires a selection among thousands of 2-D coordinates. It is thus uneconomical to invest too many learning resources for such trivial factors.
To address these challenges, we propose to model the action structure with hand-tuned rules. By doing so, the available actions are reduced to a tractable number of macro-actions, and the controller for the overall decision making system is easier to design. Along this line of thought, we implemented two AI agents. One agent adopts a reinforcement learning based controller over pre-defined macro actions (Section 3.2), while the other employs a macro-micro hierarchical action space with a rule based controller (Section 3.3). Execution of the actions relies on a per-unit-control interface of the SC2 game, which is implemented in our PySC2 extension (Section 3.1).
3.1 Our PySC2 Extension
SC2LE sc2le is a platform jointly developed by DeepMind and Blizzard. The game core library provided by Blizzard exposes a raw interface and a feature map interface. The DeepMind PySC2 environment further wraps the core library in Python and fully exposes the feature map interface. The purpose is to closely mimic human controls (e.g., a mouse click, or pressing some keyboard button), which introduces a huge number of actions due to the complexity of SC2. It thus poses difficulties for the underlying decision making system. Moreover, such a “player-level” modeling is inconvenient for designing “unit-level” models, especially when multiple agents are considered. In this work, we make additional efforts to expose unit level controls. Also, we encode the aforementioned building dependencies into a technology tree.
Expose unit control. In our PySC2 extension, we expose the raw interface of the SC2 core library, which enables per unit observations and manipulations. At each game step, all units visible to the player (depending on whether fog-of-war is enabled) can be retrieved. Each unit is fully described by a property list, including properties such as its position and health, etc. Such a raw unit array is part of the observation returned to the agent. Meanwhile, a per unit action is allowed to control each unit. The agent can send raw action commands to interested individual units (e.g., to move a unit to somewhere, or to ask a unit to attack another unit, etc.). The definition of a unit and per-unit-actions can be found in the protobuf from the SC2 core library.
Encode the technology tree. In Starcraft II, a player may need particular units (or buildings or techs) as prerequisites for other advanced units (or buildings or techs). Following UAlbertaBot churchill2016heuristic , we formalize these dependencies into a technology tree, abbreviated as TechTree in our PySC2 extension. We have collected the complete TechTree for Zerg, which gives the cost, building time, building ability, builder, prerequisites for each Zerg unit.
Besides the two additional functions described above, our PySC2 extension is fully compatible with the original Deepmind PySC2.
3.2 TStarBot1: A Macro Action Based Reinforcement Learning Agent
We illustrate in Figure 2 how the agent works. At the top, there is a single global controller, which will be learned by RL and it makes decisions over macro actions that are exposed to it. At the bottom, there is a pool of macro actions, which hard-codes prior knowledge of game rules (e.g. TechTree) and how actions are executed (e.g., which drone builds and where to build for a building action). Therefore it hides trivial decision factors and executing details from the top-level controller.
With this architecture, we relieve the underlying learning algorithm from the heavy burden of directly handling a massive number of atomic operations, while still preserving most of the key decision flexibilities of the full-game’s macro strategies. Moreover, such an agent can be equipped with basic knowledge of hard game rules without learning. With such an abstraction of action space enriched with prior-knowledge, the agent can learn fast from scratch and beat the most difficult built-in bots within days of training over a single GPU. More details are provided in the following subsections.
3.2.1 Macro Actions
We designed 165 macro actions for the Zerg-vs-Zerg SC2 full-game, as summarized in Table 1 (please refer to Appendix-I for the full list). As explained above, the purpose of the macro actions are two-fold:
To encode the game’s intrinsic rules that are difficult to learn using only the trial-and-error approach.
To hide trivial decisions from the learning algorithm by hard-coded decision making.
Each macro action executes a meaningful elementary task, e.g., to build a certain building, to produce a certain unit, to upgrade a certain technology, to harvest a certain resource, to attack a certain place, etc.. Therefore it consists of a composition or a series of atomic operations. With such an abstraction in action space, learning a high-level strategy for the full game becomes easier. Some examples of macro actions are illustrated in Table 1.
|Action Category||#||Examples||Hard-coded rules/knowledge|
|Building||13||BuildHatchery,BuildExtractor||TechTree, RandUnit, RandPlace|
|Production||22||ProduceDrone, MorphLair||TechTree, RandUnit|
|Tech Upgrading||27||UpgradeBurrow, UpgradeWeapon||TechTree, RandUnit|
|Resources Harvesting||3||CollectMinerals, InjectLarvas||RandUnit|
Building Actions: Buildings are prerequisites for further unit production and tech upgrading in SC2. The building category contains 13 macro actions, each of which builds a certain Zerg building when executed. For example, the macro action BuildSpawningPool builds a SpawningPool unit with a series of atomic ui-actions 333ui-actions refers to the actions of the ui-control interface in PySC2, resembling the human-player interface. In fact, we use in this project the unit-control interface (as described in Sec 3.1) which simplifies the execution path by allowing agents to directly push action commands to each individual unit without having to first highlight-select a subject unit before issuing a command to it.: 1) move_camera to base, 2) screen_point_select a drone (the subject unit), 3) build_spawningpool somewhere in the screen. The series of atomic operations have two internal decisions to make: 1) which Drone is used to build it? and 2) where to build it? Since these two decisions usually have little impact on the entire game process, we employ random and rule-based decision-makers, namely, a random Drone selector and a random spatial placer. The random spatial placer has to encode the basic placement rules like: Zerg buildings can only be placed on the Creep zone; Hatchery has to be located near minerals for fair harvesting efficiency. In addition, the TechTree rules such as “only Drone can build SpawningPool” are also encoded in this macro action.
Production & Tech Upgrading Actions: Unit production and tech upgrade largely shape the economy and technology development in the game. The production category contains 22 macro actions and the tech upgrade contains 27. Each of the macro actions either produces a certain type of units or upgrades a certain technology. These macro actions are hard-coded, similar to the building actions described above, except that they do not need a spatial placer.
Resource Harvesting Actions: Minerals, Gas, and Larvas (Zerg-race only) are the three key resources in SC2 games. Their storage and collection speed can greatly affect the economy growth. We designed 3 corresponding macro actions: CollectMinerals, CollectGas and InjectLarvas. CollectMinerals and CollectGas assign a certain number of random workers (i.e. Drone in Zerg-race) to mineral shards or gas extractors, so that with these two macro actions, the workers can be re-allocated to different tasks, altering the mineral and gas storage (or their ratio of storage) to meet certain needs. InjectLarvas simply orders all the idle queens to inject Larvas, with the effect of speeding up the unit production process.
Combat Actions: Combat action design is the most important aspect of SC2 AI agents, directly affecting the game’s outcome. We have carefully designed macro actions to handle the following aspects of combat strategies.
Attack timing: e.g., rush, early harass, the best attack timing windows.
Attack routes: e.g., walk around narrow slopes which might constrain the attack firepower.
Rally positions: e.g., rally before attack in order to concentrate fire (note that various units might have different moving speed).
We represent these combat strategies by region-wise macro actions, which are defined as follows (also see Figure 2). We first divide the entire world map into nine combat zones (named Zone-A to Zone-I), and an additional Zone-J for the entire world itself, resulting in 10 zones in total. Based on the zones, 100 () macro actions are defined, with each macro action executing rules such as: “combat units in Zone-X start to attack Zone-Y if there are enemies there, otherwise, rally to Zone-Y and wait”. For the micro attack tactics inside each macro action, we simply hard-code the “hit-and-run” rule for each combat unit, i.e., the unit fires at the closest enemy and runs away upon low health. We leave the investigation of more sophisticated multi-agent learning of micro tactics to future work.
With the composition of these macro actions, a wide range of diverse macro combat strategies can be represented. For example, the selection of attack routes can be represented by a series of region-wise rally macro actions. With this definition, we avoid the difficulty of complex multi-agent learning.
Available Macro Action List. Not every pre-defined macro action described above is available at any time step. For example, there are constrains in the TechTree indicating some units/techs can only be built/produced/upgraded under certain conditions: e.g., having enough storage of minerals/gas/food or the existence of certain prerequisite unit/tech. The corresponding macro action should be “do nothing” when these conditions are not satisfied. We maintain a list of such available macro actions at each time step, encoding the TechTree
knowledge. This list of available actions masks the invalid actions at each time step. The list can also be used as features for machine learning.
3.2.2 Observations and Rewards
The observations are represented as a set of spatial 2-D feature maps and a set of non-spatial scalar features, extracted from the per-unit information provided by the SC2 game core, which is exposed in our PySC2 extension (see Section3.1).
Spatial Feature Maps. Extracted feature maps are of size , where is smaller than the screen dimension. Each pixel of a feature map corresponds to a small region in the entire world map, representing a certain statistical quantity such as the unit count of a certain type in the region. These quantities include the counts of commonly-used unit types both for the player and for the opponent, and unit counts with certain attributes such as “can-attack-ground” and “can-attack-air”.
Non-spatial Features. Scalar features include the amount of gas and minerals collected, the amount of food left, the counts of each unit types, etc. They also include recently-taken actions to keep track of the past information.
Rewards. We use a ternary valued reward function: 1 (win) / 0 (tie) / -1 (loss) received at the end of a game. The reward is always zero during the game. Although the reward signal is quite sparse, and has a long time-horizon delay, it nevertheless works with the macro action structure presented in this work.
3.2.3 Learning Algorithms and Neural Network Architectures
Based on the macro actions and observations defined above, the problem becomes a sequential decision process, where the macro action space is of a tractable size and the number of macro action steps in a game is shortened from the number of original atomic actions.
At time step , an agent receives an observation from the game environment, and chooses a macro action according to its policy
, a conditional probability distribution over, where indicates the set of macro actions defined in Section 3.2.1. The selected macro action is then translated into a sequence of atomic actions that are acceptable to the game-core by using the corresponding hand-tuned rules. After the atomic actions are taken, a reward 444 The reward is accumulated within the macro action’s execution time, if the macro action lasts for multiple time-steps. and the next step observation are received by the agent. This loop goes on until the end of a game.
Our goal is to learn an optimal policy for the agent to maximize its expected cumulative rewards over all future steps. When we directly use the reward function defined in Section 3.2.2 without additional reward shaping, the optimization target is equivalent (when reward discount is ignored) to maximizing the agent’s probability of winning. We train our TStarBot1 agent to learn such a policy from scratch by playing against built-in AIs with off-the-shelf reinforcement learning algorithms (e.g. Dueling-DDQN mnih2015human ; van2016deep ; wang2016dueling and PPO schulman2017proximal ), together with a distributed rollout infrastructure. Details are presented below.
Dueling Double Deep Q-learning (DDQN). Deep Q Network mnih2015human first learns a parameterized estimation of the optimal state-action value function (Q-function) , where is the expected cumulative future rewards under policy . The optimal policy can be easily induced from the estimated optimal Q-function: if , and otherwise. Techniques such as replay memory mnih2015human , target network mnih2015human , double networks van2016deep and dueling architecture wang2016dueling
are leveraged to reduce sample correlation, maximization bias, update target inconsistency and update target variance. These techniques improve learning stability and sample efficiency. Due to the sparsity and long-delay of the rewards, we use a Mixture of Monte-Carlo (MMC)bellemare2016unifying return with the boostrapped Q-learning return as the Q update target, which accelerates the reward propagation and stabilizes the training.
Proximal Policy Optimization (PPO). We also conducted experiments by directly learning a parametric form of stochastic policy with Proximal Policy Optimization (PPO) schulman2017proximal . PPO is a sample efficient policy gradient method, leveraging policy ratio trust region clipping to avoid the complex conjugate gradient optimization required to solve the KL-divergence constrained Conservative Policy Iteration problem in TRPO schulman2015trust . We used a truncated version of generalized advantage estimation schulman2015high to trade-off the bias and variance of the advantage estimation. The available action list described in Section 3.2.1 is used to mask out unavailable actions and renormalizes the probability distributions over actions at each step.
Neural Network Architecture. We adopt multi-layer perception neural networks to parameterize the state-action value function, state value function and the policy function. While more complex network architectures could be considered (e.g., convolutional layers that extracts spatial features, or recurrent layers that compensates the partial observation), we will leave them to future work.
Distributed Rollout Infrastructure. The SC2 game core is CPU-intensive and slow for the rollout, leading to a bottleneck during the RL training. To alleviate the issue, we build a distributed rollout infrastructure, where a cluster of CPU machines (called actors) are utilized to perform the rollout processes in parallel. The rollout experiences, cached in the replay memory of each actor, are randomly sampled and periodically sent to a GPU-based machine (called learner). We currently take 1920 parallel actors (with 3840 CPUs across 80 machines) to generate the replay transitions, at the speed of about 16,000 frames per second. This significantly reduces the training time (from weeks to days), and also improves the learning stability due to the increased diversity of the explored trajectories.
3.3 TStarBot2: A Hierarchical Macro-Micro Action Based Agent
The macro action based agent described in Section 3.2 has some limitations. Although the macro actions can be grouped according to functionality, a single controller has to work over all action groups, where the actions of different groups are mutually exclusive at each decision step. Also, when predicting what action to take, the controller takes a common observation that is unaware of the action group. This amounts to unnecessary difficulties for training the controller, as undesired information may kick in for both observations and actions. Moreover, the macro action does not have any control over individual units (i.e., per-unit-control), which is inflexible when we want to adopt multi-agent style methodology.
For improved flexibility, we have created a different set of actions, as in Figure 3. We employ both macro actions and micro actions, organized in a two-tier structure. The upper tier corresponds to macro actions, which represent high-level strategies/tactics such as “build RoachWarren near our main base” or “squad one attacks enemy base”; while the lower tier corresponds to micro actions, which correspond to low-level controls over each unit such as “unit 25 builds RoachWarren at a specific position” or “unit 42 attacks to a specific position”. The entire action set is divided into groups both horizontally and vertically. Each action group is assigned a separate controller that can only see the local actions and the local observations that are relevant to the actions therein. At each time step, the controllers at the same tier can take simultaneous actions, while a downstream controller has to be conditioned on its upstream controller.
There are two advantages of this hierarchical structure. 1) Each controller has its own observation/action space so that irrelevant information can be filtered out more easily; this is also adopted and discussed in van2017hybrid when modeling the sub-task Q head. 2) The hierarchy captures the game’s action structure better, with simultaneous actions from different controllers.
Although ideally the controllers should be trained with RL either separately or jointly, in this work we simply employed expert rules, with the intention of validating the proposed hierarchical action set approach.
As in Figure 4, each controller represents a module, organized in a way similar to UAlbertaBot. The first tier modules (CombatStrategy, ProductionStrategy) only issue high-level commands (macro actions), while the second tier modules (Combat, Scout, Resource and Building) issue low-level commands (micro actions). All these modules are embedded into a DataContext, where each module can communicate with others by sending/receiving messages and sharing customized data structures. Crucially, the game-play observation exposed by PySC2 is placed in the DataContext and henceforth visible to every module. This way, each module can extract local observation relevant to its own action set from a common observation. In the following we describe the modules in greater details.
3.3.1 Data Context
The DataContext module serves as a “black board” where the modules exchange information. What contained in the DataContext fall into the following categories.
Observation. The feature maps provided by PySC2, as well as the unit data structure of all active units at the current time step, are exposed.
Pool. A pool is an array for a specific type of units, with associated properties/methods for the easy access of caller module. For example, the WorkerPool is the array of all Zerg Drones. As another example, the BasePool is the array of all Zerg bases, with each item in the pool being a BaseInstance. The BaseInstance is a customized data structure that records the Base (can be Hatchery/Hive/Lair), the associated Drones, Minerals and Extractors within a fixed range of the Base, and a local coordinate system given by the geometrical layout of the minerals and the base.
Command Queue. High level commands are stored in a queue visible to all (lower-tier) modules. For instance, the commands issued by the ProductionStrategy module are pushed in BuildCommand. A high level command may be “update a particular technology” or “harvest more minerals currently”. The lower-tier module (respectively Building and Resource in this case) will pull from the queue a command it recognizes and execute it by taking the corresponding rules to produce actions acceptable by the game core.
At each time step, the DataContext will update the Observations and various Pools, while the Command Queues will be modified or accessed by other modules.
3.3.2 Combat Strategy
The combat strategy module makes high-level decisions so that the agent can combat enemies in different ways. The module manipulates all the combat units555Currently, the combat units do not include Drones and Overlords. by organizing them into squads and armies. Each squad, which may contain one or multiple combat units, is expected to execute a specific task, such as harassing an enemy base, cleaning the rock in the map, etc. Commonly, a small group of combat units with the same unit type is organized into a squad. An army contains multiple squads with a high-level strategic objective, e.g., attacking enemy, defending base, etc., and then specific commands are sent to each squad in the army. Each command, coupled with a squad-command pair, is then pushed into a combat strategy command queue (maintained in data context), which will be received and executed by the combat module.
Our implementation includes five high-level combat strategies:
Rush: Once a squad of a small number of combat units has built up, launch attack and keep sending squads to attack the enemy base.
Economy First: Collect minerals and gases first, and then launch attack after a large number of squads have been accumulated.
Timing Attack: Build up a strong army of Roach and Hydralisk squads as quickly as possible, and initiate a strong attack.
Reform: Sort enemy bases and let the army attack the closest enemy base with high priority. When approaching the target enemy base, stop the leading squads and let them wait for other squads to gather. Then, launch attack.
Harass: Set the combat strategy for the ground combat units as ‘Reform’. Build up 2-3 squads of Mutalisk and assign a target enemy base to each of them. Then, let the Mutalisk detour and harass the Drones of the target enemy base.
The combat module fetches commands from the command queue and execute a specific action for each unit. It uses unit-level manipulation to effectively let each unit fight against the enemy. The combat module implements some basic human-like micro-management tactics, such as hit-and-run, cover-attack, etc., which can be deployed to all combat unit types. Specifically, an additional micro-management manger for each specific combat unit is implemented by taking full use of the unit-type-specific skills. For example, the Roach micro-manager enables roaches to burrow down and run away from enemy to recover when they are weak; Mutalisks are coded to stealthily reach the enemy base and harass enemy’s economy; Lurkers use carefully designed hit-and-run tactics in combination with burrowing down and up; and Queens can provide additional Larvas and cure weak allies, etc. These micro-managements are organized into hierarchies and each part can be conveniently replaced with RL models.
3.3.4 Production Strategy
The production strategy module manages the building/unit production, tech upgrading and resource harvesting. The module controls the production of units and buildings by sending production instructions to each BaseInstance. The tech upgrading instructions and other specific instructions, such as Zerg’s Morph, are pushed precisely to the target unit. Then the Building module will implement all of the above production instructions. The resource harvesting command are highly abstract that the production strategy only needs to determine what is prioritized, gas or mineral, according to the mineral/gas storage ratio. The Resource module will then re-allocate workers to each BaseInstance based on the priority instruction.
In the module, we maintain a building order queue for short-term production planning. Most time, the manager will follow the order to produce items (units, buildings or techs) as long as there are enough resources and the prerequisites are satisfied. In some special cases (e.g., expanding a new base) or in emergency situations (e.g., find cloaked enemy units), a more prioritized item can be put in front of the queue, or we can even clear the entire queue when a new goal has been set. When the queue is empty (including at the beginning of the game), a new short-term goal should be set immediately.
When executing the actual production at each time step, the prerequisites and resource requirement of the current item will be checked according to the TechTree. The prerequisites of advanced items will be added into the queue automatically if required, and the current time step will be skipped if the resource requirement is not satisfied. Moreover, when the current item is ready to be produced, a BaseInstance will be selected, informed with the item type, and assigned to perform the concrete production.
By using different opening order and goal planning functions, we have defined two different production strategies for Zerg as follows.
RUSH: “Roach rush”. It produces roaches at the beginning, and upgrades tech BURROW and TUNNELINGCLAWS to give Roaches the ability to burrow and move while burrowing, and to increase the health regeneration rate while burrowing. The strategy continuously produces Roaches and Hydralisks.
DEF_AND_ADV: "Defend and Advanced Armies". This strategy produces many SPINECRAWLERs at the second base to defend, and then gradually produces advanced armies. Almost all types of combat units are included and the final ratio among the types is restricted according to a predefined dictionary.
The building module receives and executes high level commands issued by the Production Strategy, as described in Section 3.3.4. The “unary” commands (i.e., let some unit act by itself) are straightforward to execute. Some “binary” commands require more explanations.
The command “Expand” will drag a drone from the specified base, send it to the specified “resource area”, and start morphing a Hatchery, whose global coordinate is pre-calculated by a heuristic method when the map information is obtained for the first time.
The command “Building” will drag a drone, morph it into the specified building at some position, whose coordinate is decided by a dedicated sub module, called Placer. In our implementation, we adopt a hybrid method for building placement, i.e., some of the core buildings are placed in predefined positions, while others are placed randomly. Both of these two types of positions are in the BaseInstance local coordinate system, and will be translated into the global coordinate system when they are converted into game core acceptable actions. Specifically, all tech upgrading related buildings and the first six SpineCrawlers are pre-defined. Note that the layout of the six SpineCrawlers placement is critical (e.g., whether they are in diamond formation or in rectangular formation), affecting the quality of the defense and whether we can survive an early rush of the opponent player. We have tried several arrangements and decided on the diamond formation. The other buildings, including additional SpineCrawlers, will be placed randomly, where a uniformly random coordinate is generated repeatedly until it passes all validity checking (e.g., whether it is on Zerg creep, whether it overlaps with other buildings, etc.).
The resource module is to harvest minerals and gases by sending drones to either mineral shards or extractors. At each time step, this module needs to know whether the current working mode is “mineral first” or “gas first”, which is a high level command, called “resource type priority” issued by the Production Strategy module. The goal of this module is to maximize the resource collecting speed, which can be a complex control problem. In our implementation, we adopt several rules to achieve this goal, which turns out to be simple yet effective. The underlying idea is to let every drone work and avoid any drone being idle. Specifically, we let the following rules to be executed sequentially at each time step.
Intra-base rules. At each time step, the local drones associated with a BaseInstance will be rebalanced to harvest more minerals or more gases, depending on the “resource type priority” command. Note that for each base and extractor the SC2 game core maintains two useful variables “ideal harvesters number”, which is the suggested maximum number of drones working on it, and “assigned harvesters number”, which is the actual number of drones working on it. Using these two variables, it is easy to decide whether the local working drones for minerals and gases are under-filled or over-filled.
Inter-base rules. When a new branch base is about to finish, drag 3 drones from other bases into the new base. This improves the resource collecting efficiency by saving some waiting time. We find this trick to be important, especially when expanding the first branch base.
Global rules. It scans for possible idle workers. Each idle worker is sent to the nearest base to harvest either mineral or gas, depending on the current working mode “resource type priority”. Note that when minerals or extractors are exhausted and all local drones working on them become idle, the rules also ensure that these idle workers are sent to nearby bases.
The Scout module tries to find out as many enemy units as possible. With the fog-of-war mode enabled, each unit has only a very confined view. Consequently, many enemy units are invisible, unless the player’s own units can approach them and see them via scouting.
In our implementation, we send Zerg Drones or Overlords to detect enemy units and store the discovered units in EnemyPool, from which we can infer high level information, such as the location of the enemy main base or branch base, current buildings of the enemy, etc. This kind of information can be further used to infer enemy’s strategy, useful for the CombatStrategy or ProductionStrategy to make counter-strategy accordingly.
We define the following scout tasks.
Explore Task. Whenever there is a new Overlord, we send it to a mineral zone. This action tries to look at the territory of the enemy in order to infer its economy. When attacked, the Overlord will retreat; otherwise it just stays at the target position.
Forced Task. We send a Drone to the enemy’s first branch base. By doing so, we can find out useful informations (e.g., whether a lot of enemy Zerglings have rallied, which happens when the enemy is about to perform a RUSH strategy at the early stage of the game play).
The activation of each task depends on the game’s progress and time steps.
Experimental results are reported for the two agents described in Section 3.2 and Section 3.3, respectively. We have tested the agent in a 1v1 Zerg-vs-Zerg full game. Specifically, the agent plays against the built-in AIs ranging from level 1 (the easiest) to level 10 (the hardest). The map we use is AbyssalReef666This map is an official map widely used in world class matches., on which a vanilla A3C agent over the original PySC2 observations/actions was reported sc2le , although it performed poorly when playing against built-in AIs in a Terran-vs-Terran full game.
The proposed macro-action-based agent TStarBot1(Section 3.2) is trained by playing against a mixture of built-in AIs in various difficulty levels: for each rollout episode, a difficulty level is sampled uniformly at random from level-1, 2, 4, 6, 9, 10 for the opponent built-in AI. We restrict TStarBot1 to take one macro action every 32 frames (i.e. about every 2 seconds), which shortens the time horizon to about steps per game and reduces TStarBot1’s APM (Actions Per Minute) to about , which is more comparable with that of human players. In these preliminary experiments, we only use non-spatial features together with a simple MLP neural network. Also, in order to accelerate learning, we prune the combat macro actions and we only use ZoneJ-Attack-ZoneJ, ZoneI-Attack-ZoneD, ZoneD-Attack-ZoneA.
Table 2 reports the win rates of TStarBot1 agent against built-in AI ranging from level 1 to level 10.
|Difficulty Level IDs||L-1||L-2||L-3||L-4||L-5||L-6||L-7||L-8||L-9||L-10|
Each reported win-rate is obtained by taking the mean of 200 games with different random seeds, where a tie is counted as 0.5 when calculating the win-rate. After about days of training with a single GPU and 3840 CPUs, the reinforcement learning agent (both DDQN and PPO) can win more than 90% of games against all built-in bots from level-1 to level-9, and more than 70% against level-10. The training and evaluation are both carried out with Fog-of-war enabled (no cheating).
Figure 5 shows the learning progress of TStarBot1 using the PPO algorithm. The curves show how the win-rate increases with the increased number of frames being seen during training. Each curve corresponds to a built-in AI at a certain difficulty level. Note that TStarBot1 starts to defeat (at least 75% win-rate) Easy (level-2) built-in AI at about 30M frames (about 0.06M games), Hard (level-4) at about 250M frames (about 0.5M games), VeryHard (level-6) at about 800M frames (about 1.6M games), CheatResources (level-9) at about 2000M (about 4M games), and CheatInsane (level-10) at about 3500M frames (about 7M games).
After exploration and learning, the agent seems to acquire some intriguing strategies resembling those used by human players. We demonstrate two strategies about the combat timing (i.e., when to trigger attacks) it learns, as in Figure 6: Rush, which triggers attacks as soon as possible, even if there are only a small number of combat units available; EconomyFirst, which keeps developing the economy and launches the first attack only after having assembled a strong army. Besides, we also observed that TStarBot1 tends to build bases to boost the economy growth and prefers sideways when planning for the routes of attack.
Table 2 shows the win-rates of the agent that adopts the hierarchical macro-micro action and rule based controllers, as described in Section 3.3. Each reported win-rate is obtained by taking the mean of 100 games with different random seeds, where a tie is counted as 0.5 when calculating the win-rate. Fog-of-war is enabled during the test. We can see that the agent is able to consistently defeat built-in AIs in all levels, showing the effectiveness of the hierarchical action modeling.
4.3 TStarBots vs. Human Players
In an informal internal test, we let TStarBot1 or TStarBot2 play against several human players ranging from Platinum to Diamond level in the ranking system of SC2 Battle.net League. The setting remains the same as the above sub-sections, i.e., a Zerg-vs-Zerg full game on the map AbyssalReef with fog-of-war enabled. The results are reported in Table 3. We can see that both TStarBot1 and TStarBot2 may defeat a Platinum (and even a Diamond) human player.
|#win/#loss||Platinum 1||Diamond 1||Diamond 2||Diamond 3|
4.4 TStarBot1vs. TStarBot2
In another informal test, we let the two TStarBots play against each other. We observe that TStarBot1 can always defeat TStarBot2. Inspecting the game-play, we find that TStarBot1 tends to use the Zergling Rush strategy, while TStarBot2 lacks anti-rush strategy and henceforth always loses.
It is worthy of noting that although TStarBot1 can successfully learn and acquire strategies to defeat all the built-in AIs and TStarBot2, it lacks strategy diversity in order to consistently beat human players. In the aforementioned test with human players, TStarBot1 will lose consistently once a human player figures out TStarBot1’s preference for Zergling Rush. The insufficient strategy diversity might be caused by the following reasons. 1) A lack of opponent diversity. Although the built-in AI is already equipped with several pre-defined strategies, their strategies are significantly more restricted when compared to those of human players. 2) A lack of deep exploration. The production of advanced units are buried down very deeply in the tech tree, which is difficult and inefficient for simple exploration methods such as epsilon-greedy to discover. Self-play training and randomization techniques openai-five are promising approaches to alleviate these issues, which we shall leave for future work.
For SC2LE, we have modeled the structural action space by hand-tuned rules, which reduces the large number of atomic actions into a small number of macro actions that become tractable to machine learning. We studied two agents. One agent TStarBot1, which is based on flat action modeling with a reinforcement learning controller, can achieve a reasonably high win-rate against the built-in AIs. The other agent TStarBot2, which adopts a hierarchical action modeling and rule based controllers, can consistently win against the built-in AIs. This work shows that this approach of action space modeling can be used with a conventional RL algorithm, so that the RL algorithm can learn a good policy for the SC2 1v1 full game. In the future, it will be beneficial to explore a unified approach, where the hierarchy can be optimized either jointly or separately for each component.
It is grateful that our colleagues Yan Wang and Lei Jiang, and a volunteer Yijun Huang participate the user study to test our AI agents.
- (1) Starcraft ii forum. https://eu.battle.net/forums/en/sc2/topic/853485381. Accessed August 30, 2018.
- (2) Liquipedia - ranking system. https://liquipedia.net/starcraft2/Battle.net_Leagues. Accessed August 30, 2018.
- (3) Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning, volume 1. MIT press Cambridge, 2016.
- (4) Richard S Sutton and Andrew G Barto. Introduction to reinforcement learning, volume 135. MIT press Cambridge, 1998.
- (5) David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484, 2016.
- (6) David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. Nature, 550(7676):354, 2017.
- (7) Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015.
- (8) Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski. Vizdoom: A doom-based ai research platform for visual reinforcement learning. arXiv preprint arXiv:1605.02097, 2016.
- (9) Yuxin Wu and Yuandong Tian. Training agent for first-person shooter game with actor-critic curriculum learning. In International Conference on Learning Representations, 2017.
- (10) Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al. Deepmind lab. arXiv preprint arXiv:1612.03801, 2016.
- (11) Open ai five. https://blog.openai.com/openai-five/. Accessed August 30, 2018.
- (12) Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research, 17(1):1334–1373, 2016.
- (13) Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi. Target-driven visual navigation in indoor scenes using deep reinforcement learning. International Conference on Robotics and Automation, 2017.
- (14) Fereshteh Sadeghi and Sergey Levine. (cad)2rl: Real single-image flight without a single real image. arXiv preprint arXiv:1611.04201, 2016.
- (15) Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, et al. Starcraft ii: a new challenge for reinforcement learning. arXiv preprint arXiv:1708.04782, 2017.
- (16) Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, et al. Relational deep reinforcement learning. arXiv preprint arXiv:1806.01830, 2018.
- (17) Tstarbots. https://the/link/will/be/available/in/the/next/version/of/this/manuscript/coming/soon.
- (18) Santiago Ontanón, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, and Mike Preuss. A survey of real-time strategy game ai research and competition in starcraft. IEEE Transactions on Computational Intelligence and AI in games, 5(4):293–311, 2013.
- (19) David Churchill. Heuristic Search Techniques for Real-Time Strategy Games. PhD thesis, PhD thesis, University of Alberta, 2016.
- (20) Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. In In Proceedings of the Tenth International Conference on Machine Learning, page 330–337, 1993.
- (21) Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual multi-agent policy gradients. arXiv preprint arXiv:1705.08926, 2017.
- (22) Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, and Soumith Chintala. Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks. arXiv preprint arXiv:1609.02993, 2016.
- (23) Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang. Multiagent bidirectionally-coordinated nets: Emergence of human-level coordination in learning to play starcraft combat games. arXiv preprint arXiv:1703.10069, 2017.
Sainbayar Sukhbaatar, Rob Fergus, et al.
Learning multiagent communication with backpropagation.In Advances in Neural Information Processing Systems, pages 2244–2252, 2016.
- (25) Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928–1937, 2016.
- (26) Stuart J Russell and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.
- (27) Ian Millington and John Funge. Artificial intelligence for games. CRC Press, 2009.
- (28) Alejandro Marzinotto, Michele Colledanchise, Christian Smith, and Petter Ogren. Towards a unified behavior trees framework for robot control. In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages 5420–5427. IEEE, 2014.
Diego Perez, Miguel Nicolau, Michael O’Neill, and Anthony Brabazon.
Evolving behaviour trees for the mario ai competition using
European Conference on the Applications of Evolutionary Computation, pages 123–132. Springer, 2011.
- (30) Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, and C. Lawrence Zitnick. Elf: An extensive, lightweight and flexible research platform for real-time strategy games. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 2659–2669. Curran Associates, Inc., 2017.
- (31) Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu. Feudal networks for hierarchical reinforcement learning. In International Conference on Machine Learning, pages 3540–3549, 2017.
- (32) Harm Van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, and Jeffrey Tsang. Hybrid reward architecture for reinforcement learning. In Advances in Neural Information Processing Systems, pages 5392–5402, 2017.
- (33) Richard S Sutton, Doina Precup, and Satinder Singh. Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artificial intelligence, 112(1-2):181–211, 1999.
- (34) Pierre-Luc Bacon, Jean Harb, and Doina Precup. The option-critic architecture. In AAAI, pages 1726–1734, 2017.
- (35) Behzad Ghazanfari and Matthew E Taylor. Autonomous extracting a hierarchical structure of tasks in reinforcement learning and multi-task reinforcement learning. arXiv preprint arXiv:1709.04579, 2017.
- (36) Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, et al. Strategic attentive writer for learning macro-actions. In Advances in neural information processing systems, pages 3486–3494, 2016.
- (37) Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman. Meta learning shared hierarchies. arXiv preprint arXiv:1710.09767, 2017.
- (38) Hado Van Hasselt, Arthur Guez, and David Silver. Deep reinforcement learning with double q-learning. In AAAI, volume 2, page 5. Phoenix, AZ, 2016.
- (39) Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. Dueling network architectures for deep reinforcement learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1995–2003, New York, New York, USA, 20–22 Jun 2016. PMLR.
- (40) John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
- (41) Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos. Unifying count-based exploration and intrinsic motivation. In Advances in Neural Information Processing Systems, pages 1471–1479, 2016.
- (42) John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International Conference on Machine Learning, pages 1889–1897, 2015.
- (43) John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438, 2015.
Appendix I: List of Macro Actions