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README.md

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+---
+tags:
+- ALE/SpaceInvaders-v5
+- reinforcement-learning
+- dqn
+- atari
+- gymnasium
+- pytorch
+model-index:
+- name: DQN-ALE-SpaceInvaders
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ALE/SpaceInvaders-v5
+      type: ALE/SpaceInvaders-v5
+    metrics:
+    - type: mean_reward
+      value: 528.25 +/- 111.13
+      name: mean_reward
+      verified: false
+---
+
+# Deep Q-Network (DQN) Agent playing ALE/SpaceInvaders-v5
+
+This is a trained Deep Q-Network (DQN) agent for the Atari game ALE/SpaceInvaders-v5.
+
+The model was trained using the code available [here](https://github.com/giansimone/dqn-ale-spaceinvaders/).
+
+## Usage
+To load and use this model for inference:
+
+```python
+import torch
+import json
+
+from model import DQN
+from agent import Agent
+from environment import make_env, get_env_dims
+
+#Load the configuration
+with open("config.json", "r") as f:
+    config = json.load(f)
+
+# Create environment. Get action and space dimensions
+env = make_env(config)
+state_size, action_size = get_env_dims(env)
+
+# Instantiate the agent and load the trained policy network
+agent = Agent(state_size, action_size, config)
+agent.policy_net.load_state_dict(torch.load("model.pt"))
+agent.policy_net.eval()
+
+# Enjoy the agent!
+state, _ = env.reset()
+done = False
+while not done:
+    action = agent.act(state, epsilon=0.0) # Act greedily
+    state, reward, terminated, truncated, _ = env.step(action)
+    done = terminated or truncated
+    env.render()
+```

config.json

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+{"env_id": "ALE/SpaceInvaders-v5", "frame_skip": 1, "frame_stack": 4, "resized_frame": 84, "training_steps": 10000000, "n_eval_episodes": 20, "epsilon_start": 1.0, "epsilon_end": 0.1, "anneal_steps": 1000000, "buffer_size": 100000, "batch_size": 32, "gamma": 0.99, "lr": 0.00025, "update_every": 4, "target_update_every": 10000, "max_len_window": 100, "eval_every": 50, "log_dir": "runs/", "double_dqn": false, "dueling": false, "clip_rewards": true, "seed": 42}

model.pt

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+size 6752421

replay.mp4

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+oid sha256:93641bbaf37f345cf911d2460101fa6c4dc6f62c59affe708e307b8ff97d24d2
+size 524222

results.json

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+{"env_id": "ALE/SpaceInvaders-v5", "mean_reward": 528.25, "n_eval_episodes": 20, "eval_datetime": "2025-10-22T17:42:37.136035"}