156 citations · 246 across the 43 of their papers we have counts for
10 papers · 1 filter
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Lingwei Zhu, Zheng Chen, Matthew Schlegel +1
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickl…
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entrop…
-Munchausen Reinforcement Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the curr…
AdaTerm: Adaptive T-Distribution Estimated Robust Moments for Noise-Robust Stochastic Gradient Optimization
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
With the increasing practicality of deep learning applications, practitioners are inevitably faced with datasets corrupted by noise from various sources such as measurement errors,…
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning
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…
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning
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.…
Cautious Actor-Critic
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-cr…
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport.…
Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning
Lingwei Zhu, Takamitsu Matsubara
This paper aims to establish an entropy-regularized value-based reinforcement learning method that can ensure the monotonic improvement of policies at each policy update. Unlike pr…
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +4
Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because…