This directory contains an example script that trains the Boston Dynamics Robot Atlas to walk using the environment RoboschoolAtlasForwardWalk-v1 of OpenAI Roboschool.
train_soft_actor_critic_atlas.py: Training and evalution of a Soft Actor-Critic agent
- roboschool (https://github.com/openai/roboschool)
- You can install from PyPI:
pip install roboschool
- You can install from PyPI:
Train 10M steps using 4 simulators in parallel.
python examples/atlas/train_soft_actor_critic_atlas.py --num-envs 4
Watch how the learned agent performs. <path to agent> must be a path to a directory where the agent was saved (e.g. 10000000_finish and best created inside the output directory specified as --outdir). You can specify --monitor to write video files as MP4.
python examples/atlas/train_soft_actor_critic_atlas.py --demo --render --load <path to agent> [--monitor]
Below is the learning curve of the example script with 4 CPUs and a single GPU, averaged over three trials with different random seeds. Each trial took around 71 hours for 10M steps. After every 100K timesteps, the agent is evaluated for 20 episodes.
