How does reinforcement learning through self-play contribute to the development of superhuman AI performance in classic games?
Reinforcement learning (RL) through self-play has been a pivotal methodology in achieving superhuman performance in classic games. This approach, rooted in the principles of trial and error and reward maximization, allows an artificial agent to learn optimal strategies by playing against itself. Unlike traditional supervised learning, where an algorithm learns from a labeled dataset, reinforcement
How does dynamic programming utilize models for planning in reinforcement learning, and what are the limitations when the true model is not available?
Dynamic programming (DP) is a fundamental method used in reinforcement learning (RL) for planning purposes. It leverages models to systematically solve complex problems by breaking them down into simpler subproblems. This method is particularly effective in scenarios where the environment dynamics are known and can be modeled accurately. In reinforcement learning, dynamic programming algorithms, such