Reward engineering is essential in the field of reinforcement learning, as it shapes how agents learn to make decisions based on their environment. Common techniques include designing reward functions, using shaped rewards, leveraging negative rewards, and implementing intrinsic motivation. Each of these approaches aims to guide the behavior of the learning agent in a way that aligns with predefined goals.
The first technique, designing reward functions, involves explicitly defining how rewards are assigned based on the actions taken by the agent. For instance, in a game environment, you might give positive rewards for collecting items or achieving objectives and negative rewards for actions like colliding with obstacles. This directly influences the agent's learning, as it needs to understand which actions are beneficial versus which lead to undesirable outcomes. Ideally, the reward function should be simple yet effective, capturing the essence of the tasks the agent must perform.
Another technique is using shaped rewards, which provide intermediate rewards for achieving sub-goals along the path to a main goal. For example, in a robot navigation task, rather than only rewarding the agent when it reaches the destination, you might also reward it for moving closer to the target or navigating past certain checkpoints. This helps the agent receive feedback more frequently and can lead to faster learning by reinforcing positive behavior progressively. Additionally, incorporating negative rewards or penalties can help discourage undesirable actions. For instance, if an agent runs into a wall, penalizing that action helps prevent it from repeating the mistake. Lastly, intrinsic motivation can be used to encourage exploration. By rewarding the agent for discovering new states or taking novel actions, you can ensure that it doesn’t just exploit known strategies but instead seeks to learn more about its environment.