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07/25/2023
Offline Reinforcement Learning with On-Policy Q-Function Regularization
The core challenge of offline reinforcement learning (RL) is dealing wit...
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06/24/2023
Minigrid Miniworld: Modular Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
We present the Minigrid and Miniworld libraries which provide a suite of...
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05/30/2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves su...
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04/27/2023
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sp...
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04/25/2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Auxiliary tasks improve the representations learned by deep reinforcemen...
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02/24/2023
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
In this work we identify the dormant neuron phenomenon in deep reinforce...
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06/17/2022
The State of Sparse Training in Deep Reinforcement Learning
The use of sparse neural networks has seen rapid growth in recent years,...
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06/03/2022
Beyond Tabula Rasa: Reincarnating Reinforcement Learning
Learning tabula rasa, that is without any prior knowledge, is the preval...
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11/08/2021
Losses, Dissonances, and Distortions
In this paper I present a study in using the losses and gradients obtain...
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10/26/2021
The Difficulty of Passive Learning in Deep Reinforcement Learning
Learning to act from observational data without active environmental int...
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08/30/2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated ...
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08/12/2021
A functional mirror ascent view of policy gradient methods with function approximation
We use functional mirror ascent to propose a general framework (referred...
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02/02/2021
Metrics and continuity in reinforcement learning
In most practical applications of reinforcement learning, it is untenabl...
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01/13/2021
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Reinforcement learning methods trained on few environments rarely learn ...
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11/20/2020
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Since the introduction of DQN, a vast majority of reinforcement learning...
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11/06/2020
GANterpretations
Since the introduction of Generative Adversarial Networks (GANs) [Goodfe...
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11/25/2019
Rigging the Lottery: Making All Tickets Winners
Sparse neural networks have been shown to be more parameter and compute ...
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11/21/2019
Scalable methods for computing state similarity in deterministic Markov Decision Processes
We present new algorithms for computing and approximating bisimulation m...
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07/31/2019
Inverse Reinforcement Learning with Multiple Ranked Experts
We consider the problem of learning to behave optimally in a Markov Deci...
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04/30/2019
Performing Structured Improvisations with pre-trained Deep Learning Models
The quality of outputs produced by deep generative models for music have...
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02/08/2019
Distributional reinforcement learning with linear function approximation
Despite many algorithmic advances, our theoretical understanding of prac...
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01/31/2019
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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01/31/2019
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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01/30/2019
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinfo...
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12/17/2018
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Much human and computational effort has aimed to improve how deep reinfo...
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12/14/2018
Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly i...
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11/12/2018
