Abstract

This chapter introduces causal reinforcement learning, a recent field that combines reinforcement learning and causal discovery. After a brief review of Markov decision processes and reinforcement learning, we present (i) how a causal model can be used to accelerate learning an optimal policy, and (ii) how to learn and use at the same time a causal model in the process of learning an optimal policy.