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.gitattributes

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README.md

@@ -0,0 +1,81 @@
+---
+library_name: stable-baselines3
+tags:
+- ALE/Pacman-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ALE/Pacman-v5
+      type: ALE/Pacman-v5
+    metrics:
+    - type: mean_reward
+      value: 29.90 +/- 10.35
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **ALE/Pacman-v5**
+This is a trained model of a **A2C** agent playing **ALE/Pacman-v5**
+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 ALE/Pacman-v5 -orga alfredo-wh -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env ALE/Pacman-v5  -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 ALE/Pacman-v5 -orga alfredo-wh -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env ALE/Pacman-v5  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo a2c --env ALE/Pacman-v5 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo a2c --env ALE/Pacman-v5 -f logs/ -orga alfredo-wh
+```
+
+## Hyperparameters
+```python
+OrderedDict([('env_wrapper',
+              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
+             ('frame_stack', 4),
+             ('n_envs', 16),
+             ('n_timesteps', 500000.0),
+             ('policy', 'CnnPolicy'),
+             ('policy_kwargs',
+              'dict(optimizer_class=RMSpropTFLike, '
+              'optimizer_kwargs=dict(eps=1e-5))'),
+             ('vf_coef', 0.25),
+             ('normalize', False)])
+```
+
+# Environment Arguments
+```python
+{'render_mode': 'rgb_array'}
+```

a2c-ALE-Pacman-v5.zip

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+size 13678366

a2c-ALE-Pacman-v5/_stable_baselines3_version

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+2.2.1

a2c-ALE-Pacman-v5/policy.optimizer.pth

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a2c-ALE-Pacman-v5/policy.pth

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a2c-ALE-Pacman-v5/pytorch_variables.pth

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a2c-ALE-Pacman-v5/system_info.txt

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+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.2.1
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

args.yml

@@ -0,0 +1,81 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - a2c
+  - - conf_file
+    - a2c.yml
+  - - device
+    - auto
+  - - env
+    - ALE/Pacman-v5
+  - - 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
+    - -1
+  - - 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
+    - 3695016263
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - track
+    - false
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - null
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []

config.yml

@@ -0,0 +1,15 @@
+!!python/object/apply:collections.OrderedDict
+- - - env_wrapper
+    - - stable_baselines3.common.atari_wrappers.AtariWrapper
+  - - frame_stack
+    - 4
+  - - n_envs
+    - 16
+  - - n_timesteps
+    - 500000.0
+  - - 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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+render_mode: rgb_array

replay.mp4

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+size 199686

results.json

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+{"mean_reward": 29.9, "std_reward": 10.348429832588131, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2023-11-30T19:07:37.625814"}

train_eval_metrics.zip

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