Near-Optimal Reinforcement Learning in Polynomial Time
- Cite this article as:
- Kearns, M. & Singh, S. Machine Learning (2002) 49: 209. doi:10.1023/A:1017984413808
- 988 Downloads
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 and actions, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the Exploration-Exploitation trade-off.