The Potential and Limitations of the Tsetlin Machine in Model-Free Reinforcement Learning

This paper aims to investigate the potential of model-free reinforcement learning using the Tsetlin Machine by evaluating its performance in widely recognized benchmark environments for reinforcement learning: Cartpole and Pong. Our study is divided into two primary objectives. First, we analyze the effectiveness of the Tsetlin Machine in learning from the actions of expert agents in the Cartpole environment. Second, we assess the ability of the multiclass Tsetlin Machine to learn to play both Cartpole and Pong environments from scratch. Our findings indicate that the Tsetlin Machine can successfully learn and solve the Cartpole environment. Although the Pong environment remains unsolved, t…