A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
This work addresses the problem of interoperability for researchers and practitioners in reinforcement learning, but it is incremental as it primarily reviews and organizes existing methods rather than introducing new techniques.
The authors tackled the lack of collaboration in reinforcement learning methods that use external information by proposing a conceptual framework and taxonomy for assisted reinforcement learning, which classifies and compares various approaches to improve agent performance and decision-making.
A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent's performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems.