d3rlpy: An Offline Deep Reinforcement Learning Library

2021-11-06 · Takuma Seno, Michita Imai

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}.

📄 PDF Abstract BibTeX arXiv:2111.03788

Code (2)

takuseno/d3rlpy 공식 구현 pytorch

shyamal-anadkat/offlinerl pytorch

Tasks

D4RLDeep Reinforcement LearningOffline RL reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

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Since the recent advent of deep reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have flourished. Most are model-free algorithms which can be categorized into three famili…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

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reinforcement-learning Reinforcement Learning (RL)

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Offline reinforcement learning (RL) aims to learn decision policies from a fixed batch of logged transitions, without additional environment interaction. Despite remarkable empirical progress, offline RL remains fragile …

Reinforcement Learning Offline RL

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In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning. To this end, we introduce a new …

Data Augmentation Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+2

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2022-10-13 · NeurIPS 2023 11 · Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov 외

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience w…

Benchmarking D4RL reinforcement-learning Reinforcement Learning