Transfer learning in reinforcement learning (RL) involves reusing knowledge from previously learned tasks to improve learning efficiency or performance in a new, related task. Instead of training an RL agent from scratch, transfer learning allows the agent to leverage policies, value functions, or environmental dynamics learned in a source domain to accelerate learning in a target domain. For example, an agent trained to navigate a grid-world environment could reuse its understanding of movement and obstacle avoidance to learn faster in a new maze with different layouts. This approach is particularly useful when the target task has limited training data or requires costly interactions (e.g., real-world robotics), as it reduces the need for exhaustive exploration.
A common implementation is transferring neural network weights from a pre-trained model. Suppose an RL agent uses a deep Q-network (DQN) to play a video game. The network’s early layers, which learn general features like edge detection or object tracking, can be reused as a starting point for training a similar game. Fine-tuning only the later layers (which handle game-specific decisions) can significantly cut training time. Another example is sim-to-real transfer, where a robot learns a task in a simulated environment (source) and adapts to the real world (target). Here, the agent might retain high-level strategies from simulation but adjust low-level controls to handle real-world noise. However, challenges arise if the source and target domains differ too much—for instance, if actions or state representations are incompatible—requiring careful alignment of input spaces or reward functions.
Developers can apply transfer learning in RL through frameworks like progressive networks (which scale networks for new tasks while preserving old knowledge) or meta-RL (which trains agents to adapt quickly to new tasks). For example, a robot arm trained to grasp multiple objects could use meta-RL to infer a new object’s grasping strategy within a few trials. Practical considerations include selecting relevant source tasks, freezing/sharing layers appropriately, and balancing old vs. new knowledge during fine-tuning. Libraries like RLlib support transfer by allowing policy checkpointing and reuse across experiments. While transfer learning reduces training costs, it requires testing: mismatched domains can lead to negative transfer, where prior knowledge harms performance. Developers should validate transferred models on target tasks early and adjust hyperparameters (e.g., learning rates) to stabilize adaptation.