Deep Q-Learning with Space Invaders
Hugging Face Blog
Researchers at Hugging Face present Deep Q-Learning, an extension of Q-Learning that uses neural networks to approximate Q-values instead of storing them in tables, enabling training in large state spaces like Atari games. The state space of Space Invaders is 256^100800, making a traditional Q-table with 14 states (FrozenLake) or 500 states (Taxi) impractical by comparison. The approach stabilizes training through experience replay, fixed Q-targets, and Double DQN to reduce overestimation of Q-values.
