An intrinsic reward in reinforcement learning (RL) refers to the internal motivation a learning agent receives from its actions independent of external factors or goals. Unlike extrinsic rewards, which are provided by an external environment to signal success or failure (like points or a reward structure), intrinsic rewards come from the agent's own learning process. They often aim to encourage exploration or mastery in the environment. For instance, if a robot is learning to navigate a maze, it might receive intrinsic rewards for discovering new areas or for successfully overcoming obstacles, even if these actions don't lead directly to a specified goal.
Intrinsic rewards can help improve the efficiency of the learning process. For example, consider a game-playing AI that is not immediately rewarded for winning but gains points for exploring different strategies or mastering game mechanics. This encourages the AI to try various approaches rather than just repeating the same successful action. By incorporating intrinsic rewards, the system can better understand the environment and develop more robust strategies. This is particularly useful in complex or uncertain environments, where the optimal actions are not always clear.
In practice, intrinsic rewards can take various forms. A common method in training agents is to use curiosity as an intrinsic reward; for example, an agent might be rewarded for actions that lead to new states or information. This can be implemented using techniques like novelty detection, where the agent assesses whether it is encountering new experiences. Another example is using skills acquisition as an intrinsic motivation, where agents earn rewards for improving their performance on specific tasks, thus fostering skill development. Overall, intrinsic rewards serve as vital tools for promoting exploration and effective learning within the reinforcement learning framework.