Learning by Observation: Comparison of Three Methods of Embedding Mentor's Knowledge in Reinforcement Learning Algorithms

摘要

Using knowledge of already successfully functioning agents can help to avoid expensive exploration that is so vital to some domains of reinforcement learning. Three methods to embed mentor's knowledge are proposed: initialization of Q function, reward shaping and implementing mentor's decisions in a separate action-value function. The speed of convergence of these methods in combination with Q-learning algorithm with different amount of information on mentor's decisions and their robustness to the quality of mentor are compared on four domains from the benchmarks for testing and comparing reinforcement learning algorithms "Reinforcement Learning Benchmarks and Bake-offs".