Upload trained dqn LunarLander

Belwen/dqn-LunarLander-v2.zip

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Belwen/dqn-LunarLander-v2/_stable_baselines3_version

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+2.3.2

Belwen/dqn-LunarLander-v2/data

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+    }
+}

Belwen/dqn-LunarLander-v2/policy.optimizer.pth

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Belwen/dqn-LunarLander-v2/policy.pth

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Belwen/dqn-LunarLander-v2/pytorch_variables.pth

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Belwen/dqn-LunarLander-v2/system_info.txt

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+- OS: Windows-10-10.0.22631-SP0 10.0.22631
+- Python: 3.11.9
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.3.1+cpu
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 256.63 +/- 16.61
+      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).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

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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 0x000001909456E980>", "_build": "<function DQNPolicy._build at 0x000001909456EA20>", "make_q_net": "<function DQNPolicy.make_q_net at 0x000001909456EAC0>", "forward": "<function DQNPolicy.forward at 0x000001909456EB60>", "_predict": "<function DQNPolicy._predict at 0x000001909456EC00>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x000001909456ECA0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x000001909456ED40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000019092F37200>"}, "verbose": 1, "policy_kwargs": {"net_arch": [256, 256]}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x00000190944F4E00>", "add": "<function ReplayBuffer.add at 0x00000190944F4F40>", "sample": "<function ReplayBuffer.sample at 0x00000190944F4FE0>", "_get_samples": "<function ReplayBuffer._get_samples at 0x00000190944F5080>", "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x00000190944F5120>)>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x00000190944F1000>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": 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results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.63364010000004, "std_reward": 16.611665748496495, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-21T15:50:56.076031"}