The Exploration-Exploitation Dilemma Revisited: An Entropy Perspective (2408.09974v1)

Abstract: The imbalance of exploration and exploitation has long been a significant challenge in reinforcement learning. In policy optimization, excessive reliance on exploration reduces learning efficiency, while over-dependence on exploitation might trap agents in local optima. This paper revisits the exploration-exploitation dilemma from the perspective of entropy by revealing the relationship between entropy and the dynamic adaptive process of exploration and exploitation. Based on this theoretical insight, we establish an end-to-end adaptive framework called AdaZero, which automatically determines whether to explore or to exploit as well as their balance of strength. Experiments show that AdaZero significantly outperforms baseline models across various Atari and MuJoCo environments with only a single setting. Especially in the challenging environment of Montezuma, AdaZero boosts the final returns by up to fifteen times. Moreover, we conduct a series of visualization analyses to reveal the dynamics of our self-adaptive mechanism, demonstrating how entropy reflects and changes with respect to the agent's performance and adaptive process.

• The paper introduces AdaZero, an entropy-based framework that dynamically adjusts the balance between exploration and exploitation in reinforcement learning.
• The AdaZero framework uses a state autoencoder for novelty rewards and a mastery network to adapt exploration based on state familiarity.
• AdaZero shows significant performance improvements in challenging environments, achieving up to fifteen times higher returns in Montezuma’s Revenge.

An Entropy Perspective on the Exploration-Exploitation Dilemma in Reinforcement Learning

The research paper titled "The Exploration-Exploitation Dilemma Revisited: An Entropy Perspective" presents a novel approach to addressing the longstanding challenge of balancing exploration and exploitation in reinforcement learning (RL). The exploration-exploitation trade-off is a crucial aspect of policy optimization, where excessive exploration may reduce learning efficiency, while excessive exploitation risks converging to suboptimal solutions. The authors propose an entropy-based framework, AdaZero, that dynamically adapts the balance between exploration and exploitation based on the agent’s mastery of the state space.

Theoretical Insights and AdaZero Framework

The primary theoretical contribution of the paper is the insight into how entropy relates to the intrinsic rewards commonly utilized in RL. The authors demonstrate that entropy can serve as an indicator of exploration and exploitation dynamics, with entropy levels corresponding with the presence of intrinsic rewards. This relationship is formalized into a framework called AdaZero, which consists of three main components:

1. State Autoencoder: This component provides intrinsic rewards as a proxy for state novelty by computing reconstruction errors of the observed state. A large reconstruction error signals insufficient familiarity and encourages exploration.
2. Mastery Evaluation Network: This component evaluates the level of mastery or familiarity an agent has with a given state. It outputs a probability measure that determines how much intrinsic reward should influence the policy, effectively modulating the exploration-exploitation balance.
3. Adaptive Mechanism: Using the mastery level, AdaZero adjusts intrinsic rewards dynamically, which alters the policy towards more exploration or exploitation as needed. This adaptive mechanism is fully automated, eliminating the need for manually crafted decay schemes for intrinsic rewards.

Empirical Validation and Numerical Results

The empirical evaluations exhibit AdaZero’s capability across diverse RL environments, particularly in benchmark challenges, such as Atari and MuJoCo tasks. AdaZero has demonstrated significant performance improvements over baseline models, notably in complex environments that pose significant exploration challenges. For example, in the challenging Montezuma’s Revenge environment, AdaZero improved final returns by up to fifteen times compared to baseline models. These results underscore the effectiveness of the entropy-guided adaptive mechanism in negotiating the exploration-exploitation trade-off.

Practical and Theoretical Implications

AdaZero’s proposed framework presents practical implications, suggesting that RL policy optimization can benefit from dynamically adjusting based on state familiarity without relying on pre-defined schedules or manual tuning. From a theoretical standpoint, the connection between entropy and intrinsic rewards opens new directions for understanding policy adaptation in RL.

Future Research Directions

The adoption of entropy as a balancing factor in exploration and exploitation presents several avenues for future research. Further exploration into entropy’s role within intrinsic motivation and policy robustness against environmental dynamics could yield deeper insights. Moreover, extending AdaZero’s framework to more complex, real-world applications beyond simulated environments could advance its practical utility.

Overall, this paper contributes a significant advancement in reconciling exploration and exploitation within RL systems, providing both a theoretical foundation and practical framework for enhancing policy optimization through an entropy-based adaptive mechanism.