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Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Learning to flexibly follow task instructions in dynamic environments po...
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Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help rei...
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Pairwise Weights for Temporal Credit Assignment
How much credit (or blame) should an action taken in a state get for a f...
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Learning State Representations from Random Deep Action-conditional Predictions
In this work, we study auxiliary prediction tasks defined by temporal-di...
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Efficient Querying for Cooperative Probabilistic Commitments
Multiagent systems can use commitments as the core of a general coordina...
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Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments
First-person object-interaction tasks in high-fidelity, 3D, simulated en...
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Discovering Reinforcement Learning Algorithms
Reinforcement learning (RL) algorithms update an agent's parameters acco...
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Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Deep reinforcement learning includes a broad family of algorithms that p...
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Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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Self-Tuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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How Should an Agent Practice?
We present a method for learning intrinsic reward functions to drive the...
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What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet manageme...
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Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alter...
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Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Reinforcement learning (RL) methods have been shown to be capable of lea...
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Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own ques...
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No Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
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Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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Learning Independently-Obtainable Reward Functions
We present a novel method for learning a set of disentangled reward func...
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Generative Adversarial Self-Imitation Learning
This paper explores a simple regularizer for reinforcement learning by p...
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Learning End-to-End Goal-Oriented Dialog with Multiple Answers
In a dialog, there can be multiple valid next utterances at any point. T...
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Many-Goals Reinforcement Learning
All-goals updating exploits the off-policy nature of Q-learning to updat...
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Self-Imitation Learning
This paper proposes Self-Imitation Learning (SIL), a simple off-policy a...
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Named Entities troubling your Neural Methods? Build NE-Table: A neural approach for handling Named Entities
Many natural language processing tasks require dealing with Named Entiti...
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On Learning Intrinsic Rewards for Policy Gradient Methods
In many sequential decision making tasks, it is challenging to design re...
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The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with ...
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Markov Decision Processes with Continuous Side Information
We consider a reinforcement learning (RL) setting in which the agent int...
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Value Prediction Network
This paper proposes a novel deep reinforcement learning (RL) architectur...
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Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
As a step towards developing zero-shot task generalization capabilities ...
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Repeated Inverse Reinforcement Learning
We introduce a novel repeated Inverse Reinforcement Learning problem: th...
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Control of Memory, Active Perception, and Action in Minecraft
In this paper, we introduce a new set of reinforcement learning (RL) tas...
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Towards Resolving Unidentifiability in Inverse Reinforcement Learning
We consider a setting for Inverse Reinforcement Learning (IRL) where the...
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Action-Conditional Video Prediction using Deep Networks in Atari Games
Motivated by vision-based reinforcement learning (RL) problems, in parti...
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Learning to Make Predictions In Partially Observable Environments Without a Generative Model
When faced with the problem of learning a model of a high-dimensional en...
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Approximate Planning for Factored POMDPs using Belief State Simplification
We are interested in the problem of planning for factored POMDPs. Buildi...
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On the Complexity of Policy Iteration
Decision-making problems in uncertain or stochastic domains are often fo...
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Fast Planning in Stochastic Games
Stochastic games generalize Markov decision processes (MDPs) to a multia...
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Graphical Models for Game Theory
In this work, we introduce graphical modelsfor multi-player game theory,...
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Predictive State Representations: A New Theory for Modeling Dynamical Systems
Modeling dynamical systems, both for control purposes and to make predic...
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Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Models of dynamical systems based on predictive state representations (P...
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Knowledge Combination in Graphical Multiagent Model
A graphical multiagent model (GMM) represents a joint distribution over ...
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Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
The exploreexploit dilemma is one of the central challenges in Reinforce...
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