Similarity-Based Knowledge Transfer for Cross-Domain Reinforcement Learning

In this article, we delve into the realm of reinforcement learning (RL) and its potential to address the challenges associated with transferring knowledge across different domains. The authors propose a novel approach called similarity-based knowledge transfer, which leverages the concept of similarity measures to identify good sources of knowledge for transfer. This method is rooted in the idea that if two tasks share common attributes, they can benefit from transferring knowledge from one task to another.
To comprehend this concept, imagine you’re learning a new language. You might notice that certain words or phrases have similar meanings across different languages. Similarly, in RL, tasks with similar attributes can benefit from transferring knowledge from one task to another, much like how learning a new word in one language can help you understand its equivalent in another language.
The authors present several similarity measures that can be used to compare MDPs (Markov decision processes), including model-based methods and structural similarity methods. Model-based methods compare elements of the environment dynamics, such as transition tuples or reward functions, between two MDPs. Structural similarity methods define equivalence classes based on the MDPs’ transition and reward models, such as bisimulation and MDP homomorphisms.
These similarity measures allow for identifying good sources of knowledge for transfer. By comparing an MDP with others using these similarity measures, we can determine whether they share common attributes and benefit from transferring knowledge. This approach has the potential to significantly improve the efficiency and effectiveness of RL algorithms in various domains, such as robotics, game playing, and recommendation systems.
The authors also discuss some challenges associated with this approach, including dealing with partial observability and high-dimensional state spaces. However, they provide several techniques to address these challenges, such as using embeddings or approximating the similarity measures using neural networks.
In summary, this article proposes a novel approach to transferring knowledge across different domains in RL called similarity-based knowledge transfer. By leveraging the concept of similarity measures, this approach has the potential to significantly improve the efficiency and effectiveness of RL algorithms in various domains.