Tadashi Kozuno | DeepAI

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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...

0 Côme Fiegel, et al. ∙

∙ 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...

0 Yunhao Tang, et al. ∙

∙ 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...

0 Toshinori Kitamura, et al. ∙

∙ 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...

0 Hikaru Asano, et al. ∙

∙ 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...

0 Kohei Honda, et al. ∙

∙ 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...

0 Kazumi Kasaura, et al. ∙

∙ 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...

0 Wenhao Yang, et al. ∙

∙ 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...

0 Côme Fiegel, et al. ∙

∙ 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...

0 Gellért Weisz, et al. ∙

∙ 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...

6 Tadashi Kozuno, et al. ∙

∙ 05/18/2022

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL

The performance of reinforcement learning (RL) agents is sensitive to th...

0 Han Wang, et al. ∙

∙ 07/17/2021

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences

Approximate Policy Iteration (API) algorithms alternate between (approxi...

0 Alan Chan, et al. ∙

∙ 06/24/2021

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation

Model-agnostic meta-reinforcement learning requires estimating the Hessi...

0 Yunhao Tang, et al. ∙

∙ 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...

0 Tadashi Kozuno, et al. ∙

∙ 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...

7 Hiroki Furuta, et al. ∙

∙ 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...

13 Hiroki Furuta, et al. ∙

∙ 02/27/2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning

Off-policy multi-step reinforcement learning algorithms consist of conse...

0 Tadashi Kozuno, et al. ∙

∙ 03/31/2020

Leverage the Average: an Analysis of Regularization in RL

Building upon the formalism of regularized Markov decision processes, we...

7 Nino Vieillard, et al. ∙

∙ 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...

0 Tadashi Kozuno, et al. ∙

∙ 10/30/2017

Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming

Approximate dynamic programming algorithms, such as approximate value it...

0 Tadashi Kozuno, et al. ∙

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