An artificial game playing agent can utilise either a knowledge-based method or a brute-force method to determine which move to make, depending on the type of game 
. Traditionally, brute force methods, like uninformed tree search, are effective for games with low state-space complexity whilst pure knowledge-based methods like direct coding or neural networks are generally best for games with a low decision complexity. The current state of the art game playing agent, Google DeepMind’s AlphaZero  uses a combination of brute-force and knowledge-based methods by combining tree search with a neural network. Whilst AlphaZero has demonstrated superhuman performance on a number of different games we address a weakness in the method and propose an approach which results in an improvement in the time an agent takes to learn.
Our proposed approach is based around providing a structure to the training data. Although neural networks have been shown to be universal approximators if they are large enough , large neural networks are notoriously difficult to train, and any solution using a neural networks inherits this problem. One method to address the challenge of training a large neural network is for a human to curate the training data.
AlphaZero generates its own training examples as part of its learning loop through self-play and, as such, there is no human data curation. Instead, the quality of its training examples are improved over time as the network progressively improves via a reinforcement learning process called policy iteration [4, 5]. It has been shown that a neural network can learn by minimising the difference between a prediction at one time and the same prediction at some later time, called Temporal Difference Learning ()) . It was found that rather than using a time based difference a tree search could be used to look-ahead to determine the later value called TD-Leaf  giving rise to the method used by AlphaZero. AlphaZero’s method is effective, however many of the early training examples are of inherently poor quality as they are generated using an inadequately trained111A network can be inadequately trained either due to poor training or insufficient training. neural network.
The neural network used in AlphaZero has the net effect of identifying an initial set of most promising paths for the tree search to explore. As the search progresses, if the initial most promising paths are poorly selected by the neural network they can be overridden by the search resulting in a good move decision despite the poor initial most promising set. A weakness with this approach arises when:
the network is inadequately trained, typically during the early stages of training; and
the tree expansion is less likely to discover terminal states, typically in the early stages of a game.
If the network is inadequately trained, then the set of moves that the network selects, which are intended to be promising moves, will likely be random or at best poor. If insufficient terminal states are visited during the expansion of the tree then there may not be enough actual game-environment rewards for the tree search to correct any poor network predictions. If both of these situations occur together then we say that the resulting decision is uninformed, which results in an uninformed training experience that has little or no information about the game that we are trying to learn. In this paper we demonstrate how employing an end-game-first training curriculum to exclude expectedly uninformed training experiences, results in an improved rate of learning for a combined tree-search/neural-network game playing agent.
I-a Outline of Experiment
The effect of an end-game-first
training curriculum can be achieved by discarding a fraction of the early-game experiences in each self-play game, with the fraction dependent on the number of training epochs which have occurred. By selectively discarding the early-game experiences, we hypothesise that the result is a net improvement in the quality of experiences used for training which leads to the observed improvement in the rate of learning of the player. We demonstrate the effectiveness of theend-game-first training curriculum on two games: modified Racing-Kings and Reversi. During training, we compare the win-ratio’s of two AlphaZero inspired game playing agents: a baseline player without any modification, and a player using an end-game-first curriculum (curriculum player); against a fixed reference opponent. We find that by using only the late-game data during the early training epochs, and then expanding to include earlier game experiences as the training progresses, the curriculum player learns faster compared to the baseline player. Whilst we empirically demonstrate that this method improves the player’s win-ratio over the early stages of training, the curricula used in this paper were chosen semi-arbitrarily, and as such we do not claim that the implemented curricula are optimal. We do however show that an end-game-first curriculum learning approach improves the training of a combined tree-search/neural-network game-playing reinforcement learning agent like AlphaZero.
The structure of the remainder of this paper is as follows. We review the related work in Section I-B; then outline the design of our system in Section II; present our evaluation in Section III; and finally we conclude the paper in Section IV. For ease of understanding we explain in this paper how our proposed approach enhances AlphaZero’s method and for the sake of conciseness only revisit key concepts from  where they are needed to the explain our contribution.
I-B Related Work
I-B1 AlphaZero’s Evolution
Google Deepmind’s AlphaGo surprised many researchers by defeating a professional human player at the game of Go in 2016 , however it was highly customised to the game of Go. In 2017 AlphaGo Zero was released, superseding AlphaGo with a generic algorithm with no game-specific customisation or hand-crafted features, although only tested on Go. The AlphaGo Zero method was quickly validated as being generic with the release of AlphaZero, an adaption of AlphaGo Zero for the games of Chess and Shogi . Both AlphaGo Zero and AlphaGo had a three stage training pipeline; self-play, optimization and evaluation as shown in Figure 3 and explained in Section II-B3. AlphaZero differs primarily from AlphaGo Zero in that no evaluation step is conducted as explained in Section II-B1b. While AlphaGo utilises a neural network to bias a monte-carlo-tree-search (MCTS) expansion [10, 11], AlphaGo Zero and AlphaZero
completely exclude conducting monte-carlo rollouts during the tree search, instead obtaining value estimates from the neural network. This MCTS process is explained in SectionII-B1c.
I-B2 Using a Curriculum for Machine Learning
Two predominant approaches are outlined in the literature for curriculum learning with a neural network: reward shaping and incrementally increasing problem complexity .
Reward shaping is where the reward is adjusted to encourage behaviour in the direction of the goal-state, effectively providing sub-goals which lead to the final goal. For example, an agent with an objective of moving to a particular location may be rewarded for the simpler task of progressing to a point in the direction of the final goal, but closer to the point of origin. When the sub-goal can be achieved, the reward is adjusted to encourage progression closer to the target position . Reward shaping has been used successfully to train an agent to control the flight of an autonomous helicopter in conducting highly non-linear manoeuvres [14, 15, 16].
Another approach to curriculum learning for a neural network is to incrementally increase the problem complexity as the neural network learns 
. This method has been used to train network to classifygeometric shapes by using a two step training process. In this approach, a simpler training set was used to initially train the network, before further training the network with the complete dataset which contained additional classes. This approach resulted in an improved rate of learning  for a simple neural network classifier. Both reward shaping and incrementally increasing problem complexity rely on prior knowledge of the problem and some level of human customisation [19, 20].
We introduce a new curriculum learning paradigm for machine learning by first learning the consequence of actions near a terminal state/goal-state, and then progressively learning from experiences that are further and further from the terminal state. We call this an end-game-first curriculum. The end-game-first curriculum differs from incrementally increasing the problem complexity in that the consequences of actions leading to a terminal state may in fact be more complex to learn than the earlier transitions. The end-game-first curriculum does however temporarily reduce the size of the problem space by initially training the network on a smaller subset of the overall problem space.
The advantage of an end-game-first curriculum is that it doesn’t rely on any prior knowledge of the problem. By first focusing near a terminal state, the agent is trained to recognise the features and actions which give rise to environmental rewards (terminal states) then progressively learns how to behave further and further from these states. It is not a requirement of the end-game-first curriculum that the agent commence near a terminal state, but in the course of exploration when a terminal state is discovered, a decision can be made as to which training examples will be retained depending on the the distance an experience is away from a terminal state. We demonstrate in this paper that using an end-game-first curriculum for training a combined tree-search/neural-network game playing agent can improve the rate at which the agent learns.
Ii System Design
The system consists of two core modules: the game environment and the players. Both the environment and the players utilise a standard framework, regardless of the game or the type of player. The system is designed in such a way as to reduce the variability between compared players. The agent’s performance during both training and gameplay is traded off in favour of reducing the variability of the experiments. For example, the training pipeline used for the neural network is conducted sequentially, however better performance could be obtained by conducting the training in parallel. Likewise, when two players are compared they are both trained on the same system simultaneously to further reduce the impact of any variance in processor load. The result of this design decision is that the complexity of the agent, the difficulty of the games and the quality of the opponents are constrained to permit the simultaneous training of two AI agent’s, using sequential processes, within a period of time which is reasonable yet is still sufficiently complex to demonstrate the effectiveness of the presented method. Our focus is not on the absolute performance of the agent with respect to any particular game, but instead the comparative improvement of using anend-game-first training curriculum.
Ii-a Game Environment
The game environment encodes the rules and definitions of the game as well as maintains the progress of the game, permitting players to make moves and updating the game state accordingly. The players query the game environment to inform their decisions. A game has a set of possible actions, . For any game state, the environment provides:
a tensor of sufficient size to represent the current state;
a bit array of length with bits representing the legal actions set to 1;
a vector representing the set of legal actionsin a format accepted by the environment’s move function - internally mapped to ; NB that is used to denote the set of all actions while is used to denote a single action; and
a scalar indicating if the game: is ongoing, a draw, player 1 wins, or player 2 wins.
We evaluate the effectiveness of using an end-game-first curriculum on two games; a modified version of Racing Kings  and Reversi . The games selected have fundamental differences in how a player makes their move. Reversi is a game which the board starts nearly empty and as the game progresses, tiles are placed on the board filling the board up until the game ends; tiles are not relocated once they are placed. Games like Tic-Tac-Toe, Connect Four, Hex and Go have the same movement mechanics as Reversi and with the exception of the occasional piece removal in Go, these games also fill the board as the game progresses. Racing Kings was chosen as the representative of the class of games that have piece mobility, i.e. where a piece is moved by picking it up from one cell and placing it at another. Like Racing Kings, often games with piece mobility also have the characteristic of piece removal through capture. Games with similar movement mechanics as Racing Kings includes the many Chess variants, Shogi and Nim.
II-A1a Racing Kings
Racing Kings is a game played on a Chess board with Chess pieces. Pieces are placed on a single row at one end of the board with the aim being to be the first player to move their King to the other end of the board. Checkmate is not permitted, and neither is castling, however pieces can be captured and removed from the board. We modify the full Racing Kings game by using less pieces and adjusting the starting position of the pieces by placing them in the middle of the board, as shown in Figure 1, instead of on the first rank. As the Racing Kings game library is part of a suite of Chess variants  we maintain the environment as it would be for Chess. A state is represented as a tensor of size ; the width and height dimensions representing cells on the board and the planes representing each of the player’s pieces; King, Queen, Rook, Bishop, Knight and Pawn. As the pieces are moved from one cell to another the total number of actions includes all possible pick and place options is, , excluding any additional actions such as promotion of pawns. There are possible from/to pawn promotion movements in Chess but this also needs to be multiplied by the number of pieces which a pawn can be promoted to. We only consider promotion to a Knight or a Queen giving possible promotion actions. The total number of actions for the Racing Kings game environment is .
Reversi “is a strategic boardgame which involves play by two parties on an eight-by-eight square grid with pieces that have two distinct sides … a light and a dark face. There are 64 identical pieces called ‘disks’. The basic rule of Reversi, if there are player’s discs between opponent’s discs, then the discs that belong to the player become the opponent’s discs”. The winner is the player with the most tiles when both players have no further moves. For the Reversi game environment a state is represented as a tensor of size ; the width and height dimensions representing cells on the board and each plane representing the location of each players’ pieces, with 64 possible different actions . Our Reversi environment is based on .
Ii-B Player Architecture
Two Artificial Intelligence (AI) players: a baseline player without any modifications, and a curriculum player using a specified end-game-first training curriculum, are tested in a contest against a game specific reference opponent.
Ii-B1 Artificial Intelligence Players
The architecture of both players is a combined neural network/MCTS reinforcement learning system which chooses an action from the legal moves for any given state . The neural network with parameters is trained by self-play reinforcement learning. As the players are inspired by AlphaZero, only the components relevant to this paper will be covered in this section.
II-B1a Neural Network
A deep residual convolutional neural network is used for both players with parameters as shown in TablesI and II. Deep residual convolutional neural networks have been found to be stable and less prone to overfitting than traditional convolutional neural networks for large networks. The architecture is shown in Figure 2.
The input to the neural network is a state tensor from the game environment. The neural network has two outputs, a policy vector of length , and a scalar value estimate for the given . is an sized vector, indexed by actions
, representing the probability distribution of the best actions to take from.
II-B1b Training Pipeline
The training pipeline consists of two independent processes; self-play and optimisation. The initial neural network weights are randomised and after each training iteration, , are updated yielding . The self-play process plays games against itself using the latest weights to generate training examples, filling an experience buffer. The optimisation process trains the network using these experiences via batched gradient descent. The process is shown in Figure 3(a).
Unlike AlphaZero where the processes are executed in parallel across multiple systems for maximum performance, we seek to reduce the variability of training by conducting the training process sequentially on a single system. When the experience buffer contains the experiences from a set number of games, self-play stops and optimisation begins. After optimisation has finished all experiences from a percentage of the earliest games are removed from the experience buffer, and self-play recommences.
Training experiences are generated during self-play using the latest network weights. Each training experience is comprised of a set where is the tensor representation of the state, is a probability distribution of most likely actions (policy) indexed by obtained from the MCTS, and is the scalar reward from the perspective of the player for the game’s terminal state. During self-play, an experience is saved for every ply222The term ‘ply’ is used to disambiguate one player’s turn which has different meanings in different games. One ply is a player’s single action. in the game, creating an experience buffer full of experiences from a number of different self-play games. where is the reward for the terminal state of the game and is for a loss, for a win, and for a draw. We use a slightly negative reward for a draw instead of to discourage the search from settling on a draw, instead preferring exploration of other nodes which are predicted as having a slightly negative value.
During training, the experiences are randomised, and parameters are updated to minimise the difference between and , and maximise similarities between and
using the loss function shown in Equation1. One training step is the presentation of a single batch of experiences for training, while an epoch is completed when all experiences in the buffer have been utilised. epochs are conducted for each training iteration . The experiences are stored in the experience buffer in the order in which they were created. At the conclusion of each iteration the buffer is partially emptied by removing all experiences from a portion of the oldest games.
|The reward from the game’s terminal state (return).|
|L2 weight regularisation.|
|Neural network value inference.|
|Policy from the Monte-Carlo Tree Search (MCTS).|
|Neural network policy inference.|
For completeness we also separately conduct an additional experiment using AlphaGo Zero’s method of adding a third process to evaluate the best weights . The primary difference for this experiment is that self-play is conducted with the best weights instead of the current weights, as shown in Figure 3(b).
II-B1c Monte-Carlo Tree Search (MCTS)
The MCTS builds an asymmetric tree with the states as nodes, and actions as edges. At the conclusion of the search the policy is generated by calculating the proportion of the number of visits to each edge.
During the tree search the following variables are stored:
The number of times an action was taken .
The neural network’s estimation of the optimum policy and the network’s estimate of the node’s value .
The value of the node .
The average action-value for a particular action from a node .
MCTS is conducted as follows:
Expansion. When a leaf node is found and are obtained from the neural network and a node is added to the tree.
Evaluation. If the node is terminal the reward is obtained from the environment for the current player and otherwise . Note that there is no Monte Carlo roll-out.
Backpropogation. is updated by the weighted average of the old value and the new value using Equation 4.
|Average action value.|
|Policy for from neural network.|
|The Dirichlet noise function.|
|with added Dirichlet noise.|
|Total visits to parent node.|
|Number of visits to edge ().|
|The estimated value of the node.|
At the conclusion of the search a probability distribution is calculated from the proportion of visits to each edge . The tree is retained for reuse in the player’s next turn after it is trimmed to the relevant portion based on the opponent’s action.
II-B1d Move Selection
To ensure move diversity during each self-play game, for a given number of ply as detailed in Tables I and II, actions are chosen probabilisticly based on after the MCTS is conducted. After a given number of ply, actions are then chosen in a greedy manner by selecting the action with the largest probability from . In a competition against a reference opponent, moves are always selected greedily.
Ii-B2 Curriculum Player
The difference between the baseline player and the curriculum player is that some experiences are excluded from the experience buffer during training of the curriculum player. A curriculum function is introduced which indicates the percentage of experiences to be retained for a given game, depending on the number of epochs which have occurred. When storing a game’s experiences the first of experiences are excluded from a game. This can be achieved by either trimming the game’s experiences or not generating the experience in the first place. The baseline player has an equivalent function for all ; that is retaining of a game’s experiences. The curriculum used for Racing Kings is shown in Equation 5, while the curriculum used for Reversi is shown in Equation 6.
We exploit the fact that the curriculum player excludes some early-game experiences during training by not conducting an MCTS for moves which will result in an experience that is going to be excluded. Instead of naively conducting a full MCTS and discarding the experiences we randomly choose early moves for the estimated number that would have been discarded. We manage the number of random moves by maintaining the average ply, , in a game, and playing of the moves randomly. After these random moves, we then use the full MCTS to choose the remaining actions to play the game. If a terminal state is found during random play then the game is rolled back ply and MCTS is used for the remaining moves.
Ii-B3 AlphaGo Zero Inspired Player
The AlphaGo Zero inspired player is very similar to the player explained above (see Section II-B2), with the exception of an evaluation step in the training loop. The evaluation step plays a two player competition between the current best player with weights and a challenger using the latest weights . The competition is stopped when the lower bound of the percentile Wilson confidence score with continuity correction is above , or the upper bound is below
allowing a competition winner to be declared. The competition is also stopped when the difference between the upper and lower confidence interval is less than, in which case no replacement is conducted. If the challenger is declared the winner of the competition, then it’s weights become the best weights and are used for subsequent self-play until they are replaced after a future evaluation competition. Although this method for stopping does not provide exactly confidence, it provides sufficient precision for determining which weights to use to create self-play training examples.
Ii-B4 Reference Opponent
Two AI players are trained: one using the proposed curriculum learning approach (curriculum player), the other without (baseline player). A competition is periodically conducted during training between the AI players and a reference opponent, and the players’ respective win ratios are recorded. A competition consists of a minimum of 30 games for each randomly selected to obtain the win ratio. The experiment is conducted in full three times and the results are combined. The moving average of the win ratio from all three experiments is plotted against time, steps and epochs. A training step is completed after the presentation of one batch of experiences and a training epoch is completed after all experiences have been presented once. As training begins after a set number of games, the number of steps for each epoch varies from experiment to experiment, likewise the time taken to play a game is also completely unique making the time, epochs and steps independent from experiment to experiment. As such, when combining data from multiple experiments the three plots may have different appearances.
Whilst vs time is the measure which we are primarily interested in, measuring against steps and epochs are also informative. We mitigate the potential differences which might arise from differing system loads by training both AI players simultaneously on one dual-GPU system.
Figure 4 shows the win ratio of the AlphaZero inspired players in a Racing Kings competition against the Stockfish opponent; Figure 5 shows the win ratio of the AlphaZero inspired players playing Reversi against the MCTS opponent; and Figure 6 shows the win ratio whilst playing Racing Kings against the Stockfish opponent but using an AlphaGoZero inspired player with the added evaluation step.
The win ratio of the player using the end-game-first training curriculum exceeds the baseline player during the early stages of training in all cases when measured against time. This was also observed across multiple training runs with differing network parameters and differing curricula.
Our results indicate that a player trained using an end-game-first curriculum learns faster than a player with no curriculum during the early training periods. The improvement over time can, in part, be attributed to the increased speed of self-play moves being chosen randomly by the curriculum player instead of conducting a full search; however the win ratio improvement is also observed when compared over training steps indicating that a more subtle benefit is obtained. The win ratio of the two players when compared against training epoch shows similar performance for the two players.
In this section we compare and contrast the performance of the curriculum player and the baseline player against the reference opponents and separately discuss the results with respect to time (Subsection III-B1), training steps (Subsection III-B2) and epochs (Subsection III-B3).
Iii-B1 Win ratio vs Time comparison
Subfigure (a) of Figures 4, 5 and 6 shows the win ratio of the AI players vs Time. The curriculum player does not retain experiences generated early in a game during the early training periods. This is achieved by selecting these moves randomly instead of using a naive approach of conducting a full search, and dropping the early-game experience as explained in Section II-B2. The time saved as a result of randomly selecting moves instead of conducting a tree search can be significant, however by using random move selection no experience is added to the experience buffer meaning that a randomly selected move contributes in no way to the training of the player. The curriculum needs to balance the time saved by playing random moves with the reduction in generating training experiences.
Whilst the curriculum player leads in win ratio compared to the baseline player during the early time periods, the win ratios of both players converge in all experiments. For a given neural network configuration there is some expected maximum performance threshold against a fixed opponent; in the ideal case this would be a 100% win ratio but if the network is inadequate it may be less. Although we expect that the win ratios of the two players would converge eventually, it appears that convergence occurs prior to the maximum performance threshold as shown in Figure 4(a) near minutes and Figure 6(a) near minutes; while Figure 5(a) shows convergence near minutes at what appears to be the networks maximum performance threshold. For an optimal curriculum, the convergence of the two players would be expected to occur only at the maximum performance threshold.
Iii-B2 Win ratio vs Steps comparison
Subfigure (b) in Figures 4, 5 and 6 shows the win ratio of the AI players vs the number of training steps. Although the win ratio improvement over time for the curriculum player can be attributed in part to the use of random move selection, a win ratio improvement is also observed when measured against training steps. A training step is when one batch of experiences is presented to the optimise module, making a training step time independent; i.e. how long it takes to create an experience is not a factor for training steps.
Given that the win ratio for the curriculum player outperforms the baseline player when measured over training steps, we argue that there is a gain which relates directly to the net quality of the training experiences. Consider, for example, the baseline player’s very first move decision in the very first self-play game for a newly initialised network. With a small number of MCTS simulations relative to the branching factor of the game, the first AI player’s decision will not build a tree of sufficient depth to reach a terminal node, meaning that the decision will be derived solely from the untrained network weights. The resulting policy which is stored in the experience buffer has no relevance to the game environment as it is solely reflective of the untrained network. We posit that excluding these uninformed experiences results in a net improvement in the quality of examples in the training buffer. Later in that first game, terminal states will eventually be explored and the resulting policy will become reflective of the actual game environment - these are experiences which should be retained. As the training progresses the network is able to make more accurate predictions further and further from a terminal state creating a visibility horizon which becomes larger after each epoch. The optimum curriculum would match the change of the visibility horizon.
Iii-B3 Win ratio vs Epoch comparison
Subfigure (c) in Figures 4, 5 and 6 shows the win ratio of the players vs the number of training epochs. Recall that an epoch is when all experiences in the experience buffer have been presented to the optimise module. Since training is only conducted when the experience buffer has sufficient games, an epoch is directly proportional to the number of self-play games played, but is independent of the number of experiences in the buffer and the time it takes to play a game. When applying the curriculum, fewer experiences per game are stored during the early training periods compared to the baseline player, meaning that the curriculum player is trained with fewer training experiences during early epochs.
The plots of the win ratio vs epoch shows the curriculum player outperforming the baseline player for the early epochs in Figure 4(c) with the two win ratios converging rapidly; while Figures and 5(c) and 6(c) show the two players performing similarly. The similarity of the results when measured against epochs shows that despite the curriculum player excluding experiences, no useful information is lost in doing so. In recognising that useful information is not excluded during these early epochs it supports our view that the net quality of the data in the experience buffer is improved by applying the specified end-game-first curriculum.
It is expected that due to a combination of the game mechanics and the order in which a network learns there may be some learning resistance333To our knowledge, the term learning resistance is not defined in relation to machine learning. We define it to mean a short term resistance to network improvement. which could result in plateaus in the win ratio plot, allowing the trailing player to catch up temporarily. We expect a sub-optimal curriculum to result in additional learning resistance or in the extreme case learning loss which would predominantly be observed immediately following a change in the curriculum value. For Reversi the final curriculum increment from Equation 6 occurs after 500 Epochs and Figure 5(c) shows a training plateau shortly after this change, albeit at the network’s maximum learning limit. Likewise in Figure 4(c) a learning loss is observed at an average of epochs shortly after the curriculum has changed to as shown in Equation 5, although of the three experiments that comprise the data for this plot two of them have a learning loss around epochs and the other at epochs - the final step in the curriculum. Figure 4(c) appears to indicate that on average the learning loss is caused by the final steps of the curriculum, however the learning loss was not observed in all training runs. The presence of this loss in the average of training runs, but the absence from some individual training runs highlights the importance of the order in which learning occurs and its impact on the effectiveness of the curriculum.
Iii-B4 Curriculum Considerations
Although it is expected that the baseline player’s performance would converge with the curriculum player’s performance, ideally this would occur near the maximum win ratio or at some training plateau. The fact that the player’s win ratios converge before a clear training plateau has been reached indicates that the curriculum is sub-optimal, and given that the curriculum implemented is semi-arbitrary this is expected. While curriculum learning is shown to be beneficial during the early stages of training, the gain can be lost if the curriculum changes too slowly or too abruptly.
When designing a curriculum, consideration needs to be given to the speed at which the curriculum changes. At one extreme is the current practice where the full game is attempted to be learnt at all times, i.e. the curriculum is too fast by immediately attempting to learn from of a game at epoch 0. At the other extreme is where the curriculum is too slow, which can result in the network overfitting to a small portion of the game space or discarding examples which contain useful information. We argue that each training run for each game could have its own optimum curriculum profile, due to the different training examples which are generated.
The rate at which an AI player learns when using a combined neural network-MCTS architecture can be improved by using an end-game-first training curriculum. Although the hand-crafted curricula used in this study are not optimal, a fixed curriculum is not likely to be optimal at all times as the order and the composition of the experiences are themselves a factor. The following considerations are required for an optimal curriculum;
Balancing the time saved by random moves with the loss of training experiences.
Minimising training plateaus that are not related to game complexity.
The curriculum profile changes are not too fast as to include uninformed examples.
The curriculum profile changes are not too slow as to cause the network to overfit to a smaller portion of the environment space.
The curriculum profile changes are not too slow as to result in discarding examples that are sufficiently informed.
To address these requirements, the curriculum profile should relate to the visibility horizon of the tree search, not just the number of training iterations. Our future research will explore how a curriculum can be automated based on the visibility horizon of the player’s search.
|Parameter||Value||Comment[head to column names]files/rk_params.csv|
|Parameter||Value||Comment[head to column names]files/rev_params.csv|
Computational resources and services used in this work were provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia.
-  H. van den Herik, J. W. Uiterwijk, and J. van Rijswijck, “Games solved: Now and in the future,” Artificial Intelligence, vol. 134, no. 1-2, pp. 277–311, Jan. 2002.
-  D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. P. Lillicrap, K. Simonyan, and D. Hassabis, “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” CoRR, vol. abs/1712.01815, 2017.
-  K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural Networks, vol. 4, no. 2, pp. 251–257, 2001.
-  E. J. Sondik, “The optimal control of partially observable markov processes over the infinite horizon: Discounted costs,” Operations Research, vol. 26, no. 2, pp. 282–304, 1978.
-  R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction, ser. Adaptive computation and machine learning. Cambridge, Mass: MIT Press, 1998.
-  R. S. Sutton, “Learning to predict by the methods of temporal differences,” in Machine Learning. Kluwer Academic Publishers, 1988, pp. 9–44.
-  J. Baxter, A. Tridgell, and L. Weaver, “TDLeaf(lambda): Combining temporal difference learning with game-tree search,” arXiv:cs/9901001, 1999.
-  D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
-  D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, “Mastering the game of go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354–359, 2018.
-  L. Kocsis and C. Szepesvári, “Bandit based monte-carlo planning,” in Proceedings of the 17th European Conference on Machine Learning, ser. ECML’06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 282–293.
-  C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A Survey of Monte Carlo Tree Search Methods,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1–43, Mar. 2012.
-  J. L. Elman, “Learning and development in neural networks: the importance of starting small,” Cognition, vol. 48, no. 1, pp. 71–99, 1993.
-  H. Asoh, S. Hayamizu, I. Hara, Y. Motomura, and S. Akaho, “Socially embedded learning of the office-conversant mobile robot jijo-2,” in Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, vol. 2, 1997, pp. 880–885.
-  A. Y. Ng, D. Harada, and S. J. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” in Proceedings of the Sixteenth International Conference on Machine Learning, ser. ICML ’99. Morgan Kaufmann Publishers Inc., 1999, pp. 278–287.
-  A. Y. Ng, H. J. Kim, M. I. Jordan, and S. Sastry, “Autonomous helicopter ﬂight via reinforcement learning,” Neural Information Processing Systems, vol. 16, p. 8, 2004.
-  A. Y. Ng, A. Coates, M. Diel, V. Ganapathi, J. Schulte, B. Tse, E. Berger, and E. Liang, “Autonomous inverted helicopter flight via reinforcement learning,” in Experimental Robotics IX, ser. Springer Tracts in Advanced Robotics, M. H. Ang and O. Khatib, Eds. Springer Berlin Heidelberg, 2006, pp. 363–372.
-  Y. J. Lee and K. Grauman, “Learning the easy things first: Self-paced visual category discovery,” in CVPR 2011. IEEE, 2011, pp. 1721–1728.
-  Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09. ACM Press, 2009, pp. 1–8.
-  L. Jiang, D. Meng, Q. Zhao, S. Shan, and A. G. Hauptmann, “Self-paced curriculum learning,” in AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, p. 7.
-  M. P. Kumar, B. Packer, and D. Koller, “Self-paced learning for latent variable models,” Neural Information Processing Systems, p. 9, 2010.
-  Racing kings. [Online]. Available: https://lichess.org/variant/racingKings
-  T. Landau. Othello: Brief & basic. [Online]. Available: http://www.tlandau.com/files/Othello-B&B.pdf
-  N. Fiekas. A pure python chess library with move generation and validation. [Online]. Available: https://github.com/niklasf/python-chess
-  Gunawan, H. Armanto, J. Santoso, D. Giovanni, F. Kurniawan, R. Yudianto, and Steven, “Evolutionary neural network for othello game,” Procedia - Social and Behavioral Sciences, vol. 57, pp. 419–425, 2012.
-  K. Morishita. Reversi reinforcement learning by AlphaGo zero methods.: mokemokechicken/reversi-alpha-zero. [Online]. Available: https://github.com/mokemokechicken/reversi-alpha-zero
-  K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR, vol. abs/1512.03385, 2015.
-  J. F. Reed, “Better Binomial Confidence Intervals,” Journal of Modern Applied Statistical Methods, vol. 6, no. 1, pp. 153–161, May 2007.
-  J. Frey, “Fixed-width sequential confidence intervals for a proportion,” The American Statistician, vol. 64, no. 3, pp. 242–249, 2010.
-  D. Dugovic. Multi-variant fork of popular UCI chess engine. contribute to ddugovic/stockfish development by creating an account on GitHub.