Intrinsic motivation in reinforcement learning refers to the internal drive that enables an agent to explore its environment and learn effectively without external rewards. Unlike extrinsic motivation, which relies on external incentives or feedback to guide behavior, intrinsic motivation encourages the agent to engage with its surroundings for the inherent satisfaction of learning or discovering new states and actions. This concept is particularly useful in scenarios where external rewards are sparse, delayed, or difficult to define.
A common example of intrinsic motivation in reinforcement learning is the implementation of curiosity-driven exploration. In this setup, an agent is designed to seek out novelty or uncertainty in its environment. For instance, consider a robot exploring a new room. Instead of only receiving rewards for completing specific tasks, the robot may receive intrinsic rewards for discovering new areas of the room or interacting with unfamiliar objects. This encourages it to explore more thoroughly, leading to a richer understanding of its environment and better overall performance in tasks.
Another aspect of intrinsic motivation is the idea of skill acquisition. An RL agent can be programmed to improve its strategies over time, rewarding itself for honing particular skills or optimizing its policies. For instance, in a game like chess, an agent might be intrinsically motivated to practice different opening strategies, not just to win but to enhance its understanding of the game. By focusing on mastering skills, the agent can become more adept and flexible, adapting to various scenarios it may encounter later on. In summary, intrinsic motivation fosters a more exploratory and adaptive learning approach, enhancing the agent's ability to navigate complex environments.