This branch is the code for the paper
DAC: The Double Actor-Critic Architecture for Learning Options
Shangtong Zhang, Shimon Whiteson (NeurIPS 2019)
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├── Dockerfile # Dependencies
├── requirements.txt # Dependencies
├── template_jobs.py
| ├── batch_mujoco # Start Mujoco experiments
| ├── batch_dm # Start DMControl experiments
| ├── a_squared_c_ppo_continuous # Entrance of DAC+PPO
├── deep_rl/agent/ASquaredC_PPO_agent.py # Implementation of DAC+PPO
├── deep_rl/agent/ASquaredC_A2C_agent.py # Implementation of DAC+A2C
├── deep_rl/agent/AHP_PPO_agent.py # Implementation of AHP+PPO
├── deep_rl/agent/IOPG_agent.py # Implementation of IOPG
├── deep_rl/agent/OC_agent.py # Implementation of OC
├── deep_rl/agent/PPOC_agent.py # Implementation of PPOC
├── deep_rl/component/cheetah_backward.py # Cheetah-Backward
├── deep_rl/component/walker_ex.py # Walker-Backward/Squat
├── deep_rl/component/fish_downleft.py # Fish-Downleft
└── 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.