upload via upload_folder 2025-08-03T14:19:27.385244+00:00
Browse files- README.md +48 -0
- eval_result.json +6 -0
- full_model.pt +3 -0
- params.json +40 -0
- replay.mp4 +0 -0
- state_dict.pt +3 -0
- tensorboard/events.out.tfevents.1753878949.winkindeMacBook-Air.local.77292.0 +3 -0
README.md
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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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# **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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## 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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### 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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# 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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eval_result.json
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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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full_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:2a6b059a18d14221b312a7e77230aab19092a28e8a6c002501420538847bf452
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size 283383
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params.json
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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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replay.mp4
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Binary file (45.4 kB). View file
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state_dict.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:5eac5e227b9465628923e6be71863d84ffc9bdc10bf74140ff32fb1643c1e736
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size 281335
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tensorboard/events.out.tfevents.1753878949.winkindeMacBook-Air.local.77292.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e186489638a6949b286680078163222ff04c2170e1509551005d09b1d4bc2fd
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size 1696506
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