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Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Reinforcement learning methods trained on few environments rarely learn ...
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The Importance of Pessimism in Fixed-Dataset Policy Optimization
We study worst-case guarantees on the expected return of fixed-dataset p...
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Representations for Stable Off-Policy Reinforcement Learning
Reinforcement learning with function approximation can be unstable and e...
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A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
We present a distributional approach to theoretical analyses of reinforc...
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Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces
Despite the wealth of research into provably efficient reinforcement lea...
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On Catastrophic Interference in Atari 2600 Games
Model-free deep reinforcement learning algorithms are troubled with poor...
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Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Text-based games are a natural challenge domain for deep reinforcement l...
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Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed explor...
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DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with high-dimen...
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Statistics and Samples in Distributional Reinforcement Learning
We present a unifying framework for designing and analysing distribution...
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Hyperbolic Discounting and Learning over Multiple Horizons
Reinforcement learning (RL) typically defines a discount factor as part ...
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Distributional reinforcement learning with linear function approximation
Despite many algorithmic advances, our theoretical understanding of prac...
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The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
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A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
We consider the problem of designing an artificial agent capable of inte...
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The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value ...
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A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinfo...
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Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift
In this paper we revisit the method of off-policy corrections for reinfo...
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Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly i...
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An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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Approximate Exploration through State Abstraction
Although exploration in reinforcement learning is well understood from a...
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Count-Based Exploration with the Successor Representation
The problem of exploration in reinforcement learning is well-understood ...
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An Analysis of Categorical Distributional Reinforcement Learning
Distributional approaches to value-based reinforcement learning model th...
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Distributional Reinforcement Learning with Quantile Regression
In reinforcement learning an agent interacts with the environment by tak...
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A Distributional Perspective on Reinforcement Learning
In this paper we argue for the fundamental importance of the value distr...
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The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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The Reactor: A Sample-Efficient Actor-Critic Architecture
In this work we present a new reinforcement learning agent, called React...
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Automated Curriculum Learning for Neural Networks
We introduce a method for automatically selecting the path, or syllabus,...
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Safe and Efficient Off-Policy Reinforcement Learning
In this work, we take a fresh look at some old and new algorithms for of...
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Unifying Count-Based Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem...
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Q(λ) with Off-Policy Corrections
We propose and analyze an alternate approach to off-policy multi-step te...
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Increasing the Action Gap: New Operators for Reinforcement Learning
This paper introduces new optimality-preserving operators on Q-functions...
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Compress and Control
This paper describes a new information-theoretic policy evaluation techn...
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The Arcade Learning Environment: An Evaluation Platform for General Agents
In this article we introduce the Arcade Learning Environment (ALE): both...
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