Transfer learning in reinforcement learning (RL) is a technique that allows an agent to apply knowledge gained from one task to improve performance on another, often related, task. This approach is particularly useful when the new task has limited data or resources, as it leverages past experiences to accelerate convergence and enhance learning efficiency. In essence, transfer learning helps agents build upon pre-existing skills or knowledge rather than starting from scratch.
A common method of implementing transfer learning in RL is through fine-tuning. For example, suppose an RL agent has been trained to navigate a maze efficiently. If we want it to learn to navigate a new maze with slight modifications (like some walls moved), instead of training the agent from zero, we can initialize its parameters using the learned weights from the original maze. This can significantly reduce the time taken for the new maze since the agent already understands basic navigation principles and can adapt to the changes more quickly. This type of transfer is effective when the source and target tasks share common features or environmental structures.
Another approach is to share representations or value functions between tasks. In this case, the agent may extract useful features from the state space learned during the first task and apply them to the second task. For example, if an agent learns to play multiple video games, it may recognize similar patterns or actions across these games. By sharing the learned value function or policy, the agent can generalize its knowledge and perform better on the new tasks without extensive retraining. Overall, transfer learning in RL helps create more versatile and efficient agents capable of tackling a wider range of challenges, providing a solid foundation for developing advanced applications in various environments.