The Taxonomy of Knowledge Modalities in Reinforcement Learning
Introduction to the Taxonomy
Reinforcement learning (RL) is an area of artificial intelligence that involves learning optimal behaviors through interactions with an environment to achieve certain goals. As RL expands to tackle more complex and general problems, efficient and effective transfer of knowledge gained from one setting to another has become increasingly important. Simply put, the goal is to leverage what an RL agent has learned from previous experiences (the source) to improve its performance on new, related tasks (the target).
Different Forms of Transferable Knowledge
The types of information that can be transferred in RL fall into several categories:
- Dynamics Models: These models capture how an environment changes in response to actions. They can be used in model-based planning approaches, such as model-predictive control (MPC), to predict future states and make decisions.
- Reward Models: Rewards specify the objectives within an RL problem. Transferable reward models are beneficial when target rewards are difficult to define manually or need to be inferred from data.
- Value Functions: These functions estimate the future rewards an agent can expect to receive from particular states or state-action pairs. They encompass information about system dynamics, agent behavior, and objectives, thus making them intricate to use in transfer settings.
- Policies: Policies are explicit mappings from states to actions, representing the agent's behavior. Given their direct impact on actions, they are generally the easiest to use for generating behavior but may require adaptation when there are significant changes in the dynamics or tasks.
Mechanisms for Transfer
Transfer methods can be direct (transfer and use the pre-trained models as-is) or indirect (use the pre-trained models to influence the learning of a new model).
- Direct Methods: These include fine-tuning (continue training a pre-trained model on the target task), representation transfer (reuse learned representations with additional training), and meta learning (optimize a model's ability to quickly adapt to new tasks).
- Indirect Methods: These involve leveraging data or using auxiliary objectives. For example, data collected under a policy learned in the source environment can guide exploration in the target environment.
Evaluating Transfer
Evaluating transfer can be multifaceted. It involves measuring not just the initial performance in the target domain but also how much additional experience is needed to reach a particular performance level, as well as the asymptotic performance after further training.
The Role of Data
While raw data underpins all other types of knowledge transfer, it usually requires more processing to be useful for behavior generation. Data transfer, that said, remains particularly relevant in domains without strict requirements on the speed of learning.
Comparative Analysis and Future Opportunities
General trends show a shift towards dynamics and reward models due to their potential for high generalization accuracy. However, RL faces challenges in standardizing benchmarks and evaluation protocols. The future of transfer learning in RL is likely to be shaped by larger and more diverse pre-training distributions, embodied in concepts like foundation models, and aided by trends such as modularity in learning, meta learning for adaptability, and leveraging large data sources.
Conclusion
The landscape of transfer learning in RL is rapidly evolving. By understanding how different knowledge modalities can be reused and adapted effectively, RL can move closer to achieving more general and scalable solutions across diverse tasks and environments.