Aug 14, 2023 · This paper proposes a new offline RL algorithm called, Deterministic Mixture Policy Optimization (DMPO), to overcome the issue of most existing ...
To mitigate offline RL issues, we propose an algorithm that leverages a mixture of deterministic policies. When the data distribution is multimodal, fitting a ...
Offline Reinforcement Learning with Mixture of Deterministic Policies ... PyTorch implementation of Deterministic mixture policy optimization (DMPO). If you use ...
Oct 18, 2023 · incorporates an importance weight based on the advantage function and learns the continuous latent variable. ... show in this study that LAPO ...
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting ...
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Sep 22, 2023 · Our work "Offline Reinforcement Learning with Mixture of Deterministic Policies" has been published in TMLR!
Aug 27, 2024 · Abstract:Offline reinforcement learning (RL) is a promising approach for many control applications but faces challenges such as limited data ...
Missing: Mixture | Show results with:Mixture
A concern with deterministic policies is that they are prone to overfit overestimated actions and propagate the estimation error through Bellman backups, which ...
[PDF] PLAS: Latent Action Space for Offline Reinforcement Learning
offline-rl-neurips.github.io › pdf
The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment.
Abstract. Offline Reinforcement Learning (RL) is a variant of off-policy learning where an optimal policy must be learned from a static dataset containing ...