DQN

Deep Q-Network — a value-based RL algorithm that uses a neural network to approximate the action-value function Q(s,a), selecting actions by maximizing Q. DQN introduced experience replay and target networks for stable training. While primarily used for discrete action spaces (Atari games), DQN variants have been adapted for discretized robot control.

See this in practice: our real-world evals →