Transfer Reinforcement Learning

Transfer Reinforcement Learning

Transfer Reinforcement Learning (TRL) is a subfield of machine learning that combines principles from both transfer learning and reinforcement learning. It aims to improve the efficiency of reinforcement learning algorithms by leveraging knowledge gained from previous tasks to accelerate learning in new, but related tasks.


Transfer Reinforcement Learning is a technique where an agent applies knowledge acquired from one or more source tasks to a target task. The source and target tasks are typically related but not identical. The goal is to reduce the amount of time and computational resources required to learn the target task by reusing knowledge from the source tasks.

How it Works

In a typical reinforcement learning scenario, an agent interacts with an environment to learn a policy that maximizes a reward signal. The agent starts with no knowledge of the environment and learns through trial and error. In contrast, in TRL, the agent starts with knowledge from previous tasks, which it can use to guide its learning in the new task.

The transfer can occur at different levels, including the value function level, the policy level, or the model level. The specific method of transfer depends on the similarity between the source and target tasks and the type of knowledge that is transferable.


The main benefit of TRL is that it can significantly reduce the amount of time and computational resources required to learn a new task. This is particularly useful in complex environments where traditional reinforcement learning methods can be prohibitively expensive.

TRL can also improve the performance of the agent on the target task. By leveraging knowledge from related tasks, the agent can avoid making the same mistakes and can more quickly converge to an optimal policy.


Despite its benefits, TRL also presents several challenges. One of the main challenges is determining the similarity between tasks. If the tasks are not sufficiently similar, the transferred knowledge may not be useful and could even be harmful.

Another challenge is negative transfer, where the knowledge from the source task negatively impacts the learning of the target task. This can occur if the tasks are similar in some ways but different in others, leading the agent to make incorrect assumptions about the target task.


TRL has a wide range of applications, particularly in fields where learning from scratch is computationally expensive or impractical. Examples include robotics, where TRL can be used to transfer skills between different robots or tasks, and video games, where TRL can be used to transfer knowledge between different levels or games.

  • Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.
  • Transfer Learning: A machine learning method where a pre-trained model is used as the starting point for a related task.
  • Multi-task Learning: A type of machine learning where an agent learns to perform multiple tasks simultaneously, often sharing representations between tasks.

Further Reading