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05/29/2023
VA-learning as a more efficient alternative to Q-learning
In reinforcement learning, the advantage function is critical for policy...
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05/29/2023
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
Multi-step learning applies lookahead over multiple time steps and has p...
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05/29/2023
Towards a Better Understanding of Representation Dynamics under TD-learning
TD-learning is a foundation reinforcement learning (RL) algorithm for va...
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05/28/2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
We study the problem of temporal-difference-based policy evaluation in r...
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05/22/2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...
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05/01/2023
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Representation learning and exploration are among the key challenges for...
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03/14/2023
Fast Rates for Maximum Entropy Exploration
We consider the reinforcement learning (RL) setting, in which the agent ...
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02/09/2023
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...
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01/11/2023
An Analysis of Quantile Temporal-Difference Learning
We analyse quantile temporal-difference learning (QTD), a distributional...
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12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
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07/15/2022
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
We study the multi-step off-policy learning approach to distributional R...
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06/16/2022
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for ...
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05/27/2022
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-fre...
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05/16/2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabul...
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03/30/2022
Marginalized Operators for Off-policy Reinforcement Learning
In this work, we propose marginalized operators, a new class of off-poli...
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12/14/2021
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning
Despite the empirical success of meta reinforcement learning (meta-RL), ...
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06/24/2021
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Model-agnostic meta-reinforcement learning requires estimating the Hessi...
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06/11/2021
Taylor Expansion of Discount Factors
In practical reinforcement learning (RL), the discount factor used for e...
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02/27/2021
Revisiting Peng's Q(λ) for Modern Reinforcement Learning
Off-policy multi-step reinforcement learning algorithms consist of conse...
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02/08/2021
Unlocking Pixels for Reinforcement Learning via Implicit Attention
There has recently been significant interest in training reinforcement l...
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01/19/2021
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning
We introduce ES-ENAS, a simple neural architecture search (NAS) algorith...
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07/24/2020
Monte-Carlo Tree Search as Regularized Policy Optimization
The combination of Monte-Carlo tree search (MCTS) with deep reinforcemen...
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06/13/2020
Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies
Off-policy learning algorithms have been known to be sensitive to the ch...
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06/13/2020
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
We propose a graphical model framework for goal-conditioned RL, with an ...
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06/12/2020
Self-Imitation Learning via Generalized Lower Bound Q-learning
Self-imitation learning motivated by lower-bound Q-learning is a novel a...
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03/13/2020
Taylor Expansion Policy Optimization
In this work, we investigate the application of Taylor expansions in rei...
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02/10/2020
Discrete Action On-Policy Learning with Action-Value Critic
Reinforcement learning (RL) in discrete action space is ubiquitous in re...
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09/25/2019
ES-MAML: Simple Hessian-Free Meta Learning
We introduce ES-MAML, a new framework for solving the model agnostic met...
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07/10/2019
Reinforcement Learning with Chromatic Networks
We present a new algorithm for finding compact neural networks encoding ...
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06/11/2019
Reinforcement Learning for Integer Programming: Learning to Cut
Integer programming (IP) is a general optimization framework widely appl...
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06/11/2019
Wasserstein Reinforcement Learning
We propose behavior-driven optimization via Wasserstein distances (WDs) ...
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05/29/2019
Variance Reduction for Evolution Strategies via Structured Control Variates
Evolution Strategies (ES) are a powerful class of blackbox optimization ...
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05/29/2019
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
We propose a new class of structured methods for Monte Carlo (MC) sampli...
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03/13/2019
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Due to the high variance of policy gradients, on-policy optimization alg...
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03/09/2019
Orthogonal Estimation of Wasserstein Distances
Wasserstein distances are increasingly used in a wide variety of applica...
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03/07/2019
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces
We present a new algorithm ASEBO for conducting optimization of high-dim...
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01/29/2019
Discretizing Continuous Action Space for On-Policy Optimization
In this work, we show that discretizing action space for continuous cont...
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09/27/2018
Boosting Trust Region Policy Optimization by Normalizing Flows Policy
We propose to improve trust region policy search with normalizing flows ...
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06/10/2018
Implicit Policy for Reinforcement Learning
We introduce Implicit Policy, a general class of expressive policies tha...
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05/04/2018
Exploration by Distributional Reinforcement Learning
We propose a framework based on distributional reinforcement learning an...
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11/30/2017