Distributional Reinforcement Learning

Distributional Reinforcement Learning is a reinforcement learning method that focuses on the distribution of stochastic returns Z rather than their expected values Q. By recursively describing the characteristics of the return distribution, this approach can achieve risk-aware behavior, enhancing the robustness and adaptability of decision-making. In complex environments, this method helps the agent better understand and cope with uncertainty, thereby optimizing long-term returns.