How does Transfer Learning work in RL?

Transfer Learning in reinforcement learning (RL) refers to reusing knowledge gained from solving one task (the source task) to improve learning efficiency in a related but different task (the target task). In RL, an agent learns by interacting with an environment, receiving rewards, and adjusting its policy—the strategy it uses to make decisions. Transfer Learning aims to leverage parts of this learned policy, value function (estimates of future rewards), or environment dynamics to reduce the training time or data needed for the new task. For example, an agent trained to navigate a grid-world maze might reuse its understanding of movement and obstacles when learning to navigate a differently structured maze.

There are several methods to implement Transfer Learning in RL. One common approach is parameter initialization, where neural network weights trained on the source task are used as a starting point for the target task. Fine-tuning these weights during training on the new task often leads to faster convergence. Another method involves transferring learned features or representations, such as convolutional layers in a neural network that extract useful patterns (e.g., edges in images for game-playing agents). Model-based approaches transfer knowledge about environment dynamics, such as how actions affect states, which can help the agent predict outcomes in the target task. For instance, a robot trained in simulation might use its learned model of physics to adapt more quickly to real-world conditions.

Practical examples highlight the benefits of Transfer Learning in RL. A classic use case is training an agent in a simulated environment (like a robotic arm in a physics simulator) and transferring the policy to a real-world setup, reducing the need for costly physical trials. Another example is video game agents: an AI trained on Pong might transfer its understanding of paddle movement and ball physics to a similar game like Breakout. However, success depends on task similarity—transferring between unrelated tasks can lead to negative transfer, where prior knowledge hinders learning. Developers often address this by selectively transferring components (e.g., only the feature extractor) or using meta-learning frameworks to identify shared structures across tasks. These techniques make Transfer Learning a practical tool for tackling RL’s high computational and data requirements.