RL algorithms often face challenges like limited data or computational complexity, hindering their use in real-world applications. A promising approach to address these issues is through the use of eligibility traces, which capture an agent’s experiences and guide its learning process. In this article, we explore a novel method for policy evaluation that leverages eligibility traces to improve efficiency and accuracy.
Background
RL algorithms typically learn forward value functions, which estimate the expected return of an agent’s actions. However, these methods can be computationally expensive and may not scale well with complex problems. To address this limitation, we turn to eligibility traces, which offer a way to evaluate policies more efficiently by focusing on the most relevant experiences.
Methodology
Our proposed method learns a novel value function inspired by eligibility traces. Unlike traditional approaches that learn forward value functions directly, our approach creates an intermediate representation of the value function based on the agent’s experiences. This allows us to perform policy evaluation more rapidly and with greater accuracy than traditional methods like TD(λ).
Results
We tested our method on several challenging RL problems and found that it can evaluate policies more quickly and accurately than TD(λ) in certain contexts. Our approach also offers a new perspective on eligibility traces, highlighting their potential advantages for policy evaluation.
Conclusion
Our work demonstrates the promise of eligibility traces for improving the efficiency and accuracy of RL policy evaluation. By leveraging these valuable experiences, we can develop more robust and reliable algorithms that are better equipped to handle complex real-world problems. As the field of RL continues to evolve, we expect eligibility traces to play an increasingly important role in shaping its future successes.
