Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments
This episode discusses a paper introducing Joint MDP (JMDP) formalism for reinforcement learning. The authors address a gap where standard Markov Decision Processes ignore how multiple actions interact under shared randomness. They develop a certified Bellman operator to compute complex joint statistics, demonstrating the theory works in simple environments and scales successfully to complex Atari games.