Maximizing Information in Reinforcement Learning

In reinforcement learning, solely maximizing rewards may not lead to learning good behavior. Simply maximizing rewards, like keeping a door closed continuously, may not result in actual learning. Instead, focusing on maximizing information about the environment and understanding the dynamics of the Markov decision process could lead to more meaningful and beneficial behaviors. By emphasizing learning about the environment and trajectories, agents can develop a deeper understanding and ultimately learn good behaviors more effectively than solely focusing on maximizing rewards.