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.gitattributes

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README.md

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+---
+library_name: stable-baselines3
+tags:
+- BreakoutNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BreakoutNoFrameskip-v4
+      type: BreakoutNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: 1.20 +/- 1.25
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **BreakoutNoFrameskip-v4**
+This is a trained model of a **A2C** agent playing **BreakoutNoFrameskip-v4**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo a2c --env BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4  -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo a2c --env BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo a2c --env BreakoutNoFrameskip-v4 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('ent_coef', 0.01),
+             ('env_wrapper',
+              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
+             ('frame_stack', 4),
+             ('n_envs', 32),
+             ('n_timesteps', 10000000.0),
+             ('policy', 'CnnPolicy'),
+             ('policy_kwargs',
+              'dict(optimizer_class=RMSpropTFLike, '
+              'optimizer_kwargs=dict(eps=1e-5))'),
+             ('vf_coef', 0.25),
+             ('normalize', False)])
+```

a2c-BreakoutNoFrameskip-v4.zip

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+size 13677257

a2c-BreakoutNoFrameskip-v4/_stable_baselines3_version

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+1.8.0a6

a2c-BreakoutNoFrameskip-v4/policy.optimizer.pth

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a2c-BreakoutNoFrameskip-v4/policy.pth

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a2c-BreakoutNoFrameskip-v4/pytorch_variables.pth

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a2c-BreakoutNoFrameskip-v4/system_info.txt

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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
+- Python: 3.9.12
+- Stable-Baselines3: 1.8.0a6
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.1
+- Gym: 0.21.0

args.yml

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+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - a2c
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - BreakoutNoFrameskip-v4
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 5
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 1
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - 100000
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 22871317
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - track
+    - false
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - use_envpool
+    - false
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - null
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

@@ -0,0 +1,17 @@
+!!python/object/apply:collections.OrderedDict
+- - - ent_coef
+    - 0.01
+  - - env_wrapper
+    - - stable_baselines3.common.atari_wrappers.AtariWrapper
+  - - frame_stack
+    - 4
+  - - n_envs
+    - 32
+  - - n_timesteps
+    - 10000000.0
+  - - policy
+    - CnnPolicy
+  - - policy_kwargs
+    - dict(optimizer_class=RMSpropTFLike, optimizer_kwargs=dict(eps=1e-5))
+  - - vf_coef
+    - 0.25

env_kwargs.yml

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+{}

replay.mp4

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results.json

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+{"mean_reward": 1.2, "std_reward": 1.2489995996796797, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T14:33:04.834798"}

train_eval_metrics.zip

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