| # A2C |
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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.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options |
| - also refer to the repo-wide [README.md](../../README.md#training-models) |
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| ## Files |
| - `run_atari`: file used to run the algorithm. |
| - `policies.py`: contains the different versions of the A2C architecture (MlpPolicy, CNNPolicy, LstmPolicy...). |
| - `a2c.py`: - Model : class used to initialize the step_model (sampling) and train_model (training) |
| - learn : Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm. |
| - `runner.py`: class used to generates a batch of experiences |
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