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update per-algorithm READMEs to reflect new way of running algorithms

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Peter Zhokhov
2018-08-16 12:18:06 -07:00
parent b222dd0610
commit 2614f0f65a
6 changed files with 9 additions and 8 deletions
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2
baselines/a2c/README.md
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- Original paper: https://arxiv.org/abs/1602.01783
- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
- `python -m baselines.a2c.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.

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baselines/acer/README.md
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# ACER
- Original paper: https://arxiv.org/abs/1611.01224
- `python -m baselines.acer.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=acer --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.

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baselines/acktr/README.md
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- Original paper: https://arxiv.org/abs/1708.05144
- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
- `python -m baselines.acktr.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=acktr --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.

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baselines/deepq/deepq.py
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@@ -27,7 +27,7 @@ class ActWrapper(object):
self.initial_state = None
@staticmethod
def load_act(self, path):
def load_act(path):
with open(path, "rb") as f:
model_data, act_params = cloudpickle.load(f)
act = deepq.build_act(**act_params)

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baselines/ppo2/README.md
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- Original paper: https://arxiv.org/abs/1707.06347
- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
- `python -m baselines.ppo2.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.ppo2.run_mujoco` runs the algorithm for 1M frames on a Mujoco environment.
- `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.

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baselines/trpo_mpi/README.md
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- Original paper: https://arxiv.org/abs/1502.05477
- Baselines blog post https://blog.openai.com/openai-baselines-ppo/
- `mpirun -np 16 python -m baselines.trpo_mpi.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.trpo_mpi.run_mujoco` runs the algorithm for 1M timesteps on a Mujoco environment.
- `mpirun -np 16 python -m baselines.run --alg=trpo_mpi --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=trpo_mpi --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M timesteps on a Mujoco Ant environment.