Near-Optimal Reinforcement Learning in Polynomial Time

We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy in the undiscounted case or by the horizon time T in the discounted case, we then give algorithms requiring a number of actions and total computation time that are only polynomial in T and the number of states, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the ExplorationExploitation trade-o .

• Type: Journal
• Year: 2002
• Where: ML
• Authors: Michael J. Kearns, Satinder P. Singh
• Keywords: Algorithms, General Markov Decision, Machine Learning, ML 2002, Near-optimal Return