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Reinforcement learning via conservative agent for environments with random delays

Pytorch implementation of Conservative reinforcement learning algoritm for random-delay environments.
Paper link: Conservative RL, Neural Networks 2026


Test environments

python == 3.8.0  
pytorch == 2.0.0  
mujoco == 2.2.0  
mujoco_py == 2.1.2.14  
gym == 0.26.2  

Runs

python main.py --env-name HalfCheetah-v3 --min-obs-delayed-steps 0  --max-obs-delayed-steps 10 --init-obs-delayed-steps 10 --delay-type uniform --random-seed 2026 max-step 1000000

Citation

@article{lee2026reinforcement,
  title={Reinforcement Learning via Conservative Agent for Environments with Random Delays},
  author={Lee, Jongsoo and Kim, Jangwon and Jeong, Jiseok and Han, Soohee},
  journal={Neural Networks},
  pages={108645},
  year={2026},
  publisher={Elsevier}
}

Acknowledgement

Belief Projection-based Q-learning, NeurIPS 2023

About

(Neural Networks 2026) Source-code of the paper: Conservative RL agent for environments with random delays

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