Upload DQNCartPole-v1 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 290.70 +/- 12.64
+      name: mean_reward
+      verified: false
+---
+
+# **DQN** Agent playing **CartPole-v1**
+This is a trained model of a **DQN** agent playing **CartPole-v1**
+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 0x786303c7f010>", "_build": "<function DQNPolicy._build at 0x786303c7f0a0>", "make_q_net": "<function DQNPolicy.make_q_net at 0x786303c7f130>", "forward": "<function DQNPolicy.forward at 0x786303c7f1c0>", "_predict": "<function DQNPolicy._predict at 0x786303c7f250>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x786303c7f2e0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x786303c7f370>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786303c89e80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718245312595397687, 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https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x786303c6f490>", "add": "<function ReplayBuffer.add at 0x786303c6f520>", "sample": "<function ReplayBuffer.sample at 0x786303c6f5b0>", "_get_samples": "<function ReplayBuffer._get_samples at 0x786303c6f640>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786303c70fc0>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLBGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.05, "exploration_fraction": 0.1, "target_update_interval": 625, "_n_calls": 62500, "max_grad_norm": 10, "exploration_rate": 0.05, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": 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dqn-CartPole-v1.zip

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dqn-CartPole-v1/_stable_baselines3_version

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dqn-CartPole-v1/data

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dqn-CartPole-v1/policy.optimizer.pth

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dqn-CartPole-v1/policy.pth

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dqn-CartPole-v1/pytorch_variables.pth

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dqn-CartPole-v1/system_info.txt

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+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

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+{"mean_reward": 290.7, "std_reward": 12.641598000252975, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-13T02:26:57.910692"}