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09/01/2023
Local and adaptive mirror descents in extensive-form games
We study how to learn ϵ-optimal strategies in zero-sum imperfect informa...
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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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05/22/2023
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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05/19/2023
Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation
Multi-robot navigation is the task of finding trajectories for a team of...
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04/24/2023
When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
The hierarchy of global and local planners is one of the most commonly u...
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04/18/2023
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
This study presents a benchmark for evaluating action-constrained reinfo...
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02/02/2023
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model
Robust Markov Decision Processes (MDPs) are getting more attention for l...
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12/23/2022
Adapting to game trees in zero-sum imperfect information games
Imperfect information games (IIG) are games in which each player only pa...
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10/27/2022
Confident Approximate Policy Iteration for Efficient Local Planning in q^π-realizable MDPs
We consider approximate dynamic programming in γ-discounted Markov decis...
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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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05/18/2022
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
The performance of reinforcement learning (RL) agents is sensitive to th...
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07/17/2021
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approxi...
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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
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect...
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03/31/2021
Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms
Recently many algorithms were devised for reinforcement learning (RL) wi...
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03/23/2021
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Progress in deep reinforcement learning (RL) research is largely enabled...
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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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03/31/2020
Leverage the Average: an Analysis of Regularization in RL
Building upon the formalism of regularized Markov decision processes, we...
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06/18/2019
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
In real-world applications of reinforcement learning (RL), noise from in...
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10/30/2017
