Initial commit

README.md

@@ -6,7 +6,7 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 model-index:
-- name: DQN
+- name: PPO
   results:
   - task:
       type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
       type: Acrobot-v1
     metrics:
     - type: mean_reward
-      value: -500.00 +/- 0.00
+      value: -80.30 +/- 15.87
       name: mean_reward
       verified: false
 ---
 
-# **DQN** Agent playing **Acrobot-v1**
-This is a trained model of a **DQN** agent playing **Acrobot-v1**
+# **PPO** Agent playing **Acrobot-v1**
+This is a trained model of a **PPO** agent playing **Acrobot-v1**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
 and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
 
@@ -43,39 +43,35 @@ pip install rl_zoo3
 
 ```
 # Download model and save it into the logs/ folder
-python -m rl_zoo3.load_from_hub --algo dqn --env Acrobot-v1 -orga dlantonia -f logs/
-python -m rl_zoo3.enjoy --algo dqn --env Acrobot-v1  -f logs/
+python -m rl_zoo3.load_from_hub --algo ppo --env Acrobot-v1 -orga dlantonia -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env Acrobot-v1  -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 dqn --env Acrobot-v1 -orga dlantonia -f logs/
-python -m rl_zoo3.enjoy --algo dqn --env Acrobot-v1  -f logs/
+python -m rl_zoo3.load_from_hub --algo ppo --env Acrobot-v1 -orga dlantonia -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env Acrobot-v1  -f logs/
 ```
 
 ## Training (with the RL Zoo)
 ```
-python -m rl_zoo3.train --algo dqn --env Acrobot-v1 -f logs/
+python -m rl_zoo3.train --algo ppo --env Acrobot-v1 -f logs/
 # Upload the model and generate video (when possible)
-python -m rl_zoo3.push_to_hub --algo dqn --env Acrobot-v1 -f logs/ -orga dlantonia
+python -m rl_zoo3.push_to_hub --algo ppo --env Acrobot-v1 -f logs/ -orga dlantonia
 ```
 
 ## Hyperparameters
 ```python
-OrderedDict([('batch_size', 128),
-             ('buffer_size', 50000),
-             ('exploration_final_eps', 0.1),
-             ('exploration_fraction', 0.12),
+OrderedDict([('ent_coef', 0.0),
+             ('gae_lambda', 0.94),
              ('gamma', 0.99),
-             ('gradient_steps', -1),
-             ('learning_rate', 0.00063),
-             ('learning_starts', 0),
-             ('n_timesteps', 100000.0),
+             ('n_envs', 16),
+             ('n_epochs', 4),
+             ('n_steps', 256),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
              ('policy', 'MlpPolicy'),
-             ('policy_kwargs', 'dict(net_arch=[256, 256])'),
-             ('target_update_interval', 250),
-             ('train_freq', 4),
-             ('normalize', False)])
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
 ```
 
 # Environment Arguments

args.yml

@@ -1,6 +1,6 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
-    - dqn
+    - ppo
   - - conf_file
     - null
   - - device
@@ -56,7 +56,7 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 3524042168
+    - 33537391
   - - storage
     - null
   - - study_name

config.yml

@@ -1,27 +1,19 @@
 !!python/object/apply:collections.OrderedDict
-- - - batch_size
-    - 128
-  - - buffer_size
-    - 50000
-  - - exploration_final_eps
-    - 0.1
-  - - exploration_fraction
-    - 0.12
+- - - ent_coef
+    - 0.0
+  - - gae_lambda
+    - 0.94
   - - gamma
     - 0.99
-  - - gradient_steps
-    - -1
-  - - learning_rate
-    - 0.00063
-  - - learning_starts
-    - 0
+  - - n_envs
+    - 16
+  - - n_epochs
+    - 4
+  - - n_steps
+    - 256
   - - n_timesteps
-    - 100000.0
+    - 1000000.0
+  - - normalize
+    - true
   - - policy
     - MlpPolicy
-  - - policy_kwargs
-    - dict(net_arch=[256, 256])
-  - - target_update_interval
-    - 250
-  - - train_freq
-    - 4

ppo-Acrobot-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43f5bf21c7bc419b1e2751a29d3bd77d683a65293e7215111d33b02eb65957fe
+size 147555

ppo-Acrobot-v1/_stable_baselines3_version

@@ -0,0 +1 @@
+2.4.0a7

ppo-Acrobot-v1/data

@@ -0,0 +1,115 @@
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+        "__module__": "stable_baselines3.common.policies",
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+        "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}",
+        "__doc__": "\n    Rollout buffer used in on-policy algorithms like A2C/PPO.\n    It corresponds to ``buffer_size`` transitions collected\n    using the current policy.\n    This experience will be discarded after the policy update.\n    In order to use PPO objective, we also store the current value of each state\n    and the log probability of each taken action.\n\n    The term rollout here refers to the model-free notion and should not\n    be used with the concept of rollout used in model-based RL or planning.\n    Hence, it is only involved in policy and value function training but not action selection.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n        Equivalent to classic advantage when set to 1.\n    :param gamma: Discount factor\n    :param n_envs: Number of parallel environments\n    ",
+        "__init__": "<function RolloutBuffer.__init__ at 0x7be251febf40>",
+        "reset": "<function RolloutBuffer.reset at 0x7be251ff0040>",
+        "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x7be251ff00d0>",
+        "add": "<function RolloutBuffer.add at 0x7be251ff0160>",
+        "get": "<function RolloutBuffer.get at 0x7be251ff01f0>",
+        "_get_samples": "<function RolloutBuffer._get_samples at 0x7be251ff0280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7be2520b3980>"
+    },
+    "rollout_buffer_kwargs": {},
+    "batch_size": 64,
+    "n_epochs": 4,
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+    },
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+    "normalize_advantage": true,
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+    }
+}

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+- Python: 3.10.12
+- Stable-Baselines3: 2.4.0a7
+- PyTorch: 2.3.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

results.json

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