Distributional Reduction

Distributional reinforcement learning (RL) focuses on learning the entire distribution of future rewards, not just the expected value, leading to more robust and risk-aware agents. Current research explores efficient algorithms for off-policy learning and extends distributional methods to continuous action spaces, often employing architectures like quantile regression or particle-based approximations of the reward distribution. This approach improves upon traditional RL by providing a richer understanding of uncertainty and enabling the development of more sophisticated control strategies with applications in various domains, including robotics and game playing.