🤖 AI Summary
This paper addresses the low decision quality and high error rates of controllers in real-time adaptive control. We propose an online Monte Carlo Policy Improvement (MCPI) algorithm that requires neither an environmental model nor gradient information, relying solely on a simulatable environment and an initial policy. MCPI estimates long-term action returns via parallel multi-step random rollouts and dynamically updates the policy. Its key innovation lies in directly applying a lightweight, scalable Monte Carlo Tree Search (MCTS) for online policy optimization, enabling plug-and-play reinforcement learning enhancement. Evaluated on backgammon, MCPI reduces decision error rates by over fivefold compared to baselines—including random policies and TD-Gammon—demonstrating strong generalization capability and real-time efficacy in practical adaptive control scenarios.
📝 Abstract
We present a Monte-Carlo simulation algorithm for real-time policy improvement of an adaptive controller. In the Monte-Carlo simulation, the long-term expected reward of each possible action is statistically measured, using the initial policy to make decisions in each step of the simulation. The action maximizing the measured expected reward is then taken, resulting in an improved policy. Our algorithm is easily parallelizable and has been implemented on the IBM SP1 and SP2 parallel-RISC supercomputers.
We have obtained promising initial results in applying this algorithm to the domain of backgammon. Results are reported for a wide variety of initial policies, ranging from a random policy to TD-Gammon, an extremely strong multi-layer neural network. In each case, the Monte-Carlo algorithm gives a substantial reduction, by as much as a factor of 5 or more, in the error rate of the base players. The algorithm is also potentially useful in many other adaptive control applications in which it is possible to simulate the environment.