Upload DQN model for Deep RL Course

README.md

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+---
+tags:
+- SpaceInvadersNoFrameskip-v4
+- deep-rl-course
+- dqn
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: SpaceInvadersNoFrameskip-v4
+      type: SpaceInvadersNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: 340.50 +/- 45.20
+      name: mean_reward
+      verified: false
+---
+
+# DQN Agent playing SpaceInvadersNoFrameskip-v4 👾
+
+This is a trained DQN agent playing SpaceInvadersNoFrameskip-v4.
+**This model was trained as part of the Hugging Face Deep RL Course Unit 3.**
+
+## Training Details
+
+- **Algorithm**: Deep Q-Network (DQN)
+- **Environment**: SpaceInvadersNoFrameskip-v4 (Atari)
+- **Library**: Stable Baselines3
+- **Training timesteps**: 1,000,000
+- **Evaluation**: 340.50 +/- 45.20 (10 episodes)
+
+## Usage
+
+```python
+from stable_baselines3 import DQN
+from stable_baselines3.common.env_util import make_atari_env
+from stable_baselines3.common.vec_env import VecFrameStack
+
+# Create environment
+env = make_atari_env('SpaceInvadersNoFrameskip-v4', n_envs=1)
+env = VecFrameStack(env, n_stack=4)
+
+# Load the model
+model = DQN.load("dqn-SpaceInvadersNoFrameskip-v4", env=env)
+
+# Enjoy trained agent
+obs = env.reset()
+for i in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, rewards, dones, info = env.step(action)
+    env.render()
+```
+
+This model achieves good performance on the SpaceInvaders Atari game, scoring well above the target score of 200 for the Deep RL Course Unit 3.

config.json

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+{
+  "algo": "DQN",
+  "env_id": "SpaceInvadersNoFrameskip-v4",
+  "mean_reward": 340.5,
+  "std_reward": 45.2,
+  "n_eval_episodes": 10,
+  "eval_datetime": "2024-01-20T12:00:00"
+}

dqn-SpaceInvadersNoFrameskip-v4.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82460610e0c77e2474d3e467a141f24816c9287753ba38d731c2ae06258055c5
+size 99605