Reinforcement Learning

This project compares the performance of a Q-learning based reinforcement learning approach with the basic strategy advised by online guides for learning to play Blackjack. The results show that the Q-learning algorithm significantly outperformed the basic strategy, achieving a higher win rate and average return over a large number of simulated games. The findings suggest that Q-learning can be an effective approach to learning to play Blackjack, offering a better strategy than the basic strategy recommended by online guides.

Humans posess a vast sensorimotor knowledge base that must be utilized to accelerate robot policy learning. While existing methods of demonstration generally ask humans to directly manipulate the robot’s state, this work utilizes a more natural demonstration capture platform (i.e. mouse and keyboard) to train deep learning networks on a motion planning task. Through qualitative and quantitative analysis, we demonstrate that such methods can successfully supervise imitation learning policies.