MDP Homomorphism

Markov Decision Process (MDP) homomorphisms aim to simplify complex reinforcement learning problems by creating abstract representations of the original environment. Current research focuses on extending these techniques to continuous state and action spaces, developing algorithms that learn homomorphisms automatically (e.g., using forward-backward models or bisimulation metrics), and analyzing their impact on sample efficiency. This work is significant because it promises to improve the scalability and generalization capabilities of reinforcement learning, particularly in applications like scientific discovery and robotics where high-dimensional state spaces are common.