Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization

Researchers develop Macro Action Quantization (MAQ), an AI framework that learns human-like behaviors in reinforcement learning by optimizing action trajectories. Experiments show significant improvements in human-likeness and performance across RL tasks.