Push to Hub

README.md

@@ -16,69 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -638.18 +/- 102.53
+      value: 174.28 +/- 45.84
       name: mean_reward
       verified: false
 ---
 
 # **DQN** Agent playing **LunarLander-v2**
 This is a trained model of a **DQN** agent playing **LunarLander-v2**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
-and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-The RL Zoo is a training framework for Stable Baselines3
-reinforcement learning agents,
-with hyperparameter optimization and pre-trained agents included.
+## Usage (with Stable-baselines3)
+TODO: Add your code
 
-## 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 dqn --env LunarLander-v2 -orga nsanghi -f logs/
-python -m rl_zoo3.enjoy --algo dqn --env LunarLander-v2  -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 LunarLander-v2 -orga nsanghi -f logs/
-python -m rl_zoo3.enjoy --algo dqn --env LunarLander-v2  -f logs/
-```
-
-## Training (with the RL Zoo)
-```
-python -m rl_zoo3.train --algo dqn --env LunarLander-v2 -f logs/
-# Upload the model and generate video (when possible)
-python -m rl_zoo3.push_to_hub --algo dqn --env LunarLander-v2 -f logs/ -orga nsanghi
-```
-
-## Hyperparameters
 ```python
-OrderedDict([('batch_size', 128),
-             ('buffer_size', 50000),
-             ('exploration_final_eps', 0.1),
-             ('exploration_fraction', 0.12),
-             ('gamma', 0.99),
-             ('gradient_steps', -1),
-             ('learning_rate', 0.00063),
-             ('learning_starts', 0),
-             ('n_timesteps', 100000.0),
-             ('policy', 'MlpPolicy'),
-             ('policy_kwargs', 'dict(net_arch=[256, 256])'),
-             ('target_update_interval', 250),
-             ('train_freq', 4),
-             ('normalize', False)])
-```
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
-# Environment Arguments
-```python
-{'render_mode': 'rgb_array'}
+...
 ```

config.json

@@ -1 +1 @@
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optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function DQNPolicy.__init__ at 0x7f3e88af11f0>", "_build": "<function DQNPolicy._build at 0x7f3e88af1280>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7f3e88af1310>", "forward": "<function DQNPolicy.forward at 0x7f3e88af13a0>", "_predict": "<function DQNPolicy._predict at 0x7f3e88af1430>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f3e88af14c0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f3e88af1550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3e88e829c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1709585141267487266, 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+        "__abstractmethods__": "frozenset()",
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+    "exploration_final_eps": 0.1,
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+    "max_grad_norm": 10,
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@@ -73,7 +101,7 @@
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@@ -81,47 +109,14 @@
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-        "add": "<function ReplayBuffer.add at 0x7f45ef077dc0>",
-        "sample": "<function ReplayBuffer.sample at 0x7f45ef077e50>",
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-        "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f45ef06fbc0>"
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-    "exploration_fraction": 0.12,
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