Transfer learning in reinforcement learning (RL) involves using knowledge gained from one task to improve performance on a different, but related, task. This approach is beneficial because training an RL agent can be resource-intensive, requiring substantial time and computational power. By leveraging previously learned skills or representations, developers can speed up the training process for new tasks, make it more efficient, and enhance the overall performance of the agent.
One common application of transfer learning in RL is during multi-task learning, where an agent is trained to perform several related tasks simultaneously. For instance, if an agent is trained to navigate different environments, such as a maze or a simple platformer game, it can transfer its understanding of navigation strategies from one environment to another. This means that when the agent starts training on a new maze, it begins with an existing knowledge base about spatial awareness and obstacle avoidance, which can lead to faster adaptation and better performance than if the agent started from scratch.
Another example can be seen in robotics, where a robotic arm learns to manipulate objects. If an agent has been trained to pick up and place certain objects within a known environment, it can apply this knowledge when introduced to a similar environment with new objects. The agent might leverage its prior experience with certain grasping techniques or motion patterns, allowing it to learn the new task more efficiently. Overall, transfer learning helps to share insights across related tasks, ultimately improving the effectiveness of training in reinforcement learning scenarios.