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README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - BeamRiderNoFrameskip-v4
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: A2C
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: BeamRiderNoFrameskip-v4
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+ type: BeamRiderNoFrameskip-v4
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+ metrics:
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+ - type: mean_reward
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+ value: 330.00 +/- 108.23
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **A2C** Agent playing **BeamRiderNoFrameskip-v4**
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+ This is a trained model of a **A2C** agent playing **BeamRiderNoFrameskip-v4**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m rl_zoo3.load_from_hub --algo a2c --env BeamRiderNoFrameskip-v4 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo a2c --env BeamRiderNoFrameskip-v4 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo a2c --env BeamRiderNoFrameskip-v4 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo a2c --env BeamRiderNoFrameskip-v4 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python -m rl_zoo3.train --algo a2c --env BeamRiderNoFrameskip-v4 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo a2c --env BeamRiderNoFrameskip-v4 -f logs/ -orga qgallouedec
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('ent_coef', 0.01),
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+ ('env_wrapper',
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+ ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
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+ ('frame_stack', 4),
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+ ('n_envs', 16),
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+ ('n_timesteps', 10000000.0),
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+ ('policy', 'CnnPolicy'),
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+ ('policy_kwargs',
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+ 'dict(optimizer_class=RMSpropTFLike, '
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+ 'optimizer_kwargs=dict(eps=1e-5))'),
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+ ('vf_coef', 0.25),
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+ ('normalize', False)])
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+ ```
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+ - OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
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+ - Python: 3.9.12
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+ - Stable-Baselines3: 1.8.0a6
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+ - PyTorch: 1.13.1+cu117
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+ - GPU Enabled: True
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+ - Numpy: 1.24.1
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+ - Gym: 0.21.0
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