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update REAMD.md.

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2019-03-26 03:32:11 +09:00
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baselines/ppo2/README.md
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- Original paper: https://arxiv.org/abs/1707.06347 - Original paper: https://arxiv.org/abs/1707.06347
- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/ - Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
## Examples
- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options. - `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment. - `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment.
- also refer to the repo-wide [README.md](../../README.md#training-models)
### RNN networks
- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --network=ppo_cnn_lstm` runs on an Atari Pong with
`ppo_cnn_lstm` network.
- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6 --network=ppo_lstm --value_network=copy`
runs on a Mujoco Ant environment with `ppo_lstm` network whose value and policy networks are separated, but have
same structure.
## See Also
- refer to the repo-wide [README.md](../../README.md#training-models)