This branch is the code for the paper
Deep Residual Reinforcement Learning
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson (AAMAS 2020)
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├── Dockerfile # Dependencies
├── requirements.txt # Dependencies
├── job.py
| ├── residual_ddpg_continuous # Start Bi-Res-DDPG and its variants
| ├── oracle_ddpg_continuous # Start DDPG/Dyna-DDPG/Res-Dyna-DDPG/MVE-DDPG with an oracle model
| ├── model_ddpg_continuous # Start DDPG/Dyna-DDPG/Res-Dyna-DDPG/MVE-DDPG with a learned model
├── deep_rl/agent/ResidualDDPG_agent.py # Implementation of Bi-Res-DDPG and its variants
├── deep_rl/agent/OracleDDPG_agent.py # Implementation of variants of model-based DDPG with an oracle model
├── deep_rl/agent/ModelDDPG_agent.py # Implementation of variants of model-based DDPG with a learned model
└── plot_paper.py # Plotting
I can send the data for plotting via email upon request.
This branch is based on the DeepRL codebase and is left unchanged after I completed the paper. Algorithm implementations not used in the paper may be broken and should never be used. It may take extra effort if you want to rebase/merge the master branch.
