Transfer Reinforcement Learning addresses the challenge of training agents in environments where data collection is costly or time-consuming by reusing knowledge from previous tasks. This approach involves transferring policies, value functions, or learned representations from source tasks to enhance the learning process in a new, related task. TRL methods can be categorized based on what is transferred (e.g., raw experiences, policy parameters, feature representations) and how the transfer is achieved (e.g., initialization, guiding exploration, shaping rewards). By leveraging previously acquired knowledge, TRL can significantly reduce the sample complexity and training time, leading to faster and more robust learning in the target task. Applications of TRL span various domains, including robotics, where transferring learned motor skills across different robots can expedite training, and in games, where strategies learned in one game level can aid in mastering another.
The concept of transfer learning dates back to the 1990s, but its specific application to reinforcement learning emerged in the early 2000s. The term "Transfer Reinforcement Learning" started gaining traction in academic research around 2010, reflecting growing interest in making RL systems more efficient and generalizable.
Significant contributors to the development of TRL include Andrew Ng, who has extensively worked on transfer learning and reinforcement learning, and Matthew E. Taylor, whose research has focused on transfer in RL, particularly in the context of multi-agent systems. Their contributions, among others, have helped shape the methodologies and theoretical foundations of TRL, pushing the field towards practical and scalable solutions.