rl_reach: Reproducible reinforcement learning experiments for robotic reaching tasks

Author:

Aumjaud PierreORCID,McAuliffe David,Rodríguez Lera Francisco J.,Cardiff Philip

Funder

Horizon 2020 Marie Skłodowska-Curie Actions

Publisher

Elsevier BV

Reference21 articles.

1. Deep reinforcement learning that matters;Henderson,2018

2. G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, W. Zaremba, OpenAI Gym, CoRR abs/1606.0. arXiv:1606.01540.

3. The Arcade Learning Environment : An Evaluation Platform for General Agents, vol. 47;Bellemare,2013

4. DeepMind Lab;Beattie,2016

5. Gotta learn fast: A new benchmark for generalization in RL, arxiv;Nichol,2018

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1. Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review;Energies;2023-10-19

2. Enhancing Value Estimation Policies by Post-Hoc Symmetry Exploitation in Motion Planning Tasks;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

3. Towards verifiable Benchmarks for Reinforcement Learning;2022 IEEE Conference on Games (CoG);2022-08-21

4. FRobs_RL: A Flexible Robotics Reinforcement Learning Library;2022 IEEE 18th International Conference on Automation Science and Engineering (CASE);2022-08-20

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