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upload via upload_folder 2025-08-03T14:19:27.385244+00:00

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README.md ADDED
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+ ---
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+ env_name: LunarLander-v3
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+ tags:
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+ - LunarLander-v3
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+ - dueling dqn
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+ - reinforcement-learning
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+ - custom-implementation
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+ - deep-q-learning
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+ - pytorch
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+ model-index:
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+ - name: DuelingDQN-1d-LunarLander-v3
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: LunarLander-v3
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+ type: LunarLander-v3
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+ metrics:
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+ - type: mean_reward
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+ value: 274.78 +/- 16.60
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **Dueling DQN** Agent playing **LunarLander-v3**
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+ This is a trained model of a **Dueling DQN** agent playing **LunarLander-v3**.
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+
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+ ## Usage
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+ ### create the conda env in https://github.com/GeneHit/drl_practice
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+ ```bash
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+ conda create -n drl python=3.10
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+ conda activate drl
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+ python -m pip install -r requirements.txt
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+ ```
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+
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+ ### play with full model
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+ ```python
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+ # load the full model
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+ model = load_from_hub(repo_id="winkin119/DuelingDQN-1d-LunarLander-v3", filename="full_model.pt")
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+
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+ # Create the environment.
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+ env = gym.make("LunarLander-v3")
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+ state, _ = env.reset()
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+ action = model.action(state)
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+ ...
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+ ```
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+ There is also a state dict version of the model, you can check the corresponding chapter in the repo.
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+ {
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+ "mean_reward": 274.78191540088744,
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+ "std_reward": 16.598264623349365,
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+ "datetime": "2025-07-30T12:45:02.785477+00:00",
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+ "train_duration_min": "9.05"
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+ }
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params.json ADDED
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+ {
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+ "env_config": {
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+ "env_id": "LunarLander-v3",
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+ "env_kwargs": {},
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+ "max_steps": null,
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+ "normalize_obs": false,
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+ "use_image": false,
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+ "vector_env_num": 6,
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+ "use_multi_processing": true,
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+ "image_shape": null,
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+ "frame_stack": 1,
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+ "frame_skip": 1,
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+ "training_render_mode": null
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+ },
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+ "device": "cpu",
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+ "learning_rate": 0.0003,
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+ "gamma": 0.99,
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+ "checkpoint_pathname": "",
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+ "max_grad_norm": null,
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+ "log_interval": 50,
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+ "track": true,
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+ "eval_episodes": 100,
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+ "eval_random_seed": 42,
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+ "eval_video_num": 10,
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+ "timesteps": 250000,
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+ "epsilon_schedule": {
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+ "_type": "LinearSchedule",
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+ "_module": "practice.utils_for_coding.scheduler_utils",
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+ "_start_e": 1.0,
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+ "_end_e": 0.01,
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+ "_duration": 150000,
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+ "_start_t": 0
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+ },
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+ "replay_buffer_capacity": 120000,
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+ "batch_size": 64,
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+ "train_interval": 1,
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+ "target_update_interval": 250,
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+ "update_start_step": 2000,
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+ "dqn_algorithm": "basic"
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+ }
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