Transfer Learning in RL

Learning to ride a bicycle makes learning a motorcycle easier: balance, steering, and road awareness carry over, even though the vehicle is different. Transfer learning in reinforcement learning applies that idea to agents: use experience from one task to learn another task faster, more safely, or with fewer costly reward-driven trials.

In standard RL, an agent begins each new environment with little knowledge and must discover useful behaviour through interaction. Transfer changes that starting point. A source task supplies reusable knowledge to a target task, such as:

• a pretrained policy, which proposes actions;
• a value function or critic, which estimates long-term reward;
• representations learned from observations, such as visual features;
• skills or action sequences, such as “grasp,” “walk,” or “avoid obstacle”;
• a learned dynamics model for predicting environmental changes.

The target agent then fine-tunes this knowledge using its own rewards and interactions. For example, a robot trained in simulation to pick up blocks can start real-world training with a competent grasping policy instead of performing thousands of random, potentially damaging movements.

Tasks can differ in their observations, action meanings, physics, reward goals, or episode lengths. A policy that succeeds in one setting can therefore mislead the learner in another—a problem called negative transfer. A driving agent pretrained on dry roads might brake or turn badly on ice because the familiar visual cues remain while the dynamics change. Good transfer needs enough shared structure, and it must preserve exploration so the agent can correct inherited assumptions.

Transfer is especially valuable when live trials are slow, unsafe, or expensive. In practice, PPO or DQN policies are commonly pretrained across related simulated tasks, then adapted to a target Gymnasium or MuJoCo environment. It makes reward learning more data-efficient, but it does not guarantee robustness: a high training score can hide reliance on source-specific shortcuts that collapse after a small shift in dynamics or reward design.