Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
This addresses a bottleneck in reinforcement learning efficiency for researchers and practitioners, though it appears incremental as it builds on existing MDP theory.
The paper tackles the problem of exogenous state variables and rewards slowing down reinforcement learning by formalizing them and deriving conditions for decomposition into exogenous and endogenous components, showing that this can significantly speed up learning in tested MDPs.
Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving only the exogenous state+reward and an endogenous Markov Decision Process defined with respect to only the endogenous rewards. We also derive a variance-covariance condition under which Monte Carlo policy evaluation on the endogenous MDP is accelerated compared to using the full MDP. Similar speedups are likely to carry over to all RL algorithms. We develop two algorithms for discovering the exogenous variables and test them on several MDPs. Results show that the algorithms are practical and can significantly speed up reinforcement learning.