arXiv:2111.03788
공식 코드 · Papers with Code
d3rlpy: An Offline Deep Reinforcement Learning Library
원문 초록
In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: \url{https://github.com/takuseno/d3rlpy}.
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DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Deep Reinforcement Learning with Gradient Eligibility Traces
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
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