Search Results for author: Toshinori Kitamura

Found 5 papers, 1 papers with code

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

2 code implementations8 Dec 2021 Toshinori Kitamura, Ryo Yonetani

We present ShinRL, an open-source library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives.

Q-Learning Reinforcement Learning (RL)

Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning

no code implementations16 Jul 2021 Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara

The recent boom in the literature on entropy-regularized reinforcement learning (RL) approaches reveals that Kullback-Leibler (KL) regularization brings advantages to RL algorithms by canceling out errors under mild assumptions.

reinforcement-learning Reinforcement Learning (RL)

Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning

no code implementations13 Jul 2021 Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara

In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning.

Atari Games reinforcement-learning +1

Cautious Actor-Critic

no code implementations12 Jul 2021 Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara

The oscillating performance of off-policy learning and persisting errors in the actor-critic (AC) setting call for algorithms that can conservatively learn to suit the stability-critical applications better.

Continuous Control

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