TY - GEN
T1 - Reinforcement learning with non-markovian rewards
AU - Gaon, Maor
AU - Brafman, Ronen I.
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is that the rewards depend on the last state and action only. Yet, many real-world rewards are non-Markovian. For example, a reward for bringing coffee only if requested earlier and not yet served, is non-Markovian if the state only records current requests and deliveries. Past work considered the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but we know of no principled approaches for RL with NMR. Here, we address the problem of policy learning from experience with such rewards. We describe and evaluate empirically four combinations of the classical RL algorithm Q-learning and R-max with automata learning algorithms to obtain new RL algorithms for domains with NMR. We also prove that some of these variants converge to an optimal policy in the limit.
AB - The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is that the rewards depend on the last state and action only. Yet, many real-world rewards are non-Markovian. For example, a reward for bringing coffee only if requested earlier and not yet served, is non-Markovian if the state only records current requests and deliveries. Past work considered the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but we know of no principled approaches for RL with NMR. Here, we address the problem of policy learning from experience with such rewards. We describe and evaluate empirically four combinations of the classical RL algorithm Q-learning and R-max with automata learning algorithms to obtain new RL algorithms for domains with NMR. We also prove that some of these variants converge to an optimal policy in the limit.
UR - http://www.scopus.com/inward/record.url?scp=85098401766&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85098401766
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 3980
EP - 3987
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI press
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -