Exploitation Is All You Need... for Exploration

2025-08-02

AI Summary

In meta-reinforcement learning, achieving effective exploration without explicit exploration mechanisms remains a fundamental challenge. Method: We investigate whether greedy policies can intrinsically induce exploratory behavior when environments exhibit structural regularity, agents possess memory, and long-horizon credit assignment is employed—within a memory-augmented meta-RL framework evaluated on stochastic multi-armed bandits and temporally extended grid worlds. Contribution/Results: We demonstrate that pure greedy policies spontaneously exhibit exploration under structural regularity and memory, revealing a pseudo-Thompson sampling effect that challenges the classical exploration-exploitation dichotomy. Structural regularity and memory are both necessary: removing either collapses exploration. Long-horizon credit assignment, while not strictly required, improves robustness. Our findings provide a novel perspective on intrinsic exploration mechanisms in intelligent agents, suggesting that structured environments coupled with memory can fundamentally reshape policy-level exploration behavior without dedicated exploration modules.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Reinforcement Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Ensuring sufficient exploration is a central challenge when training meta-reinforcement learning (meta-RL) agents to solve novel environments. Conventional solutions to the exploration-exploitation dilemma inject explicit incentives such as randomization, uncertainty bonuses, or intrinsic rewards to encourage exploration. In this work, we hypothesize that an agent trained solely to maximize a greedy (exploitation-only) objective can nonetheless exhibit emergent exploratory behavior, provided three conditions are met: (1) Recurring Environmental Structure, where the environment features repeatable regularities that allow past experience to inform future choices; (2) Agent Memory, enabling the agent to retain and utilize historical interaction data; and (3) Long-Horizon Credit Assignment, where learning propagates returns over a time frame sufficient for the delayed benefits of exploration to inform current decisions. Through experiments in stochastic multi-armed bandits and temporally extended gridworlds, we observe that, when both structure and memory are present, a policy trained on a strictly greedy objective exhibits information-seeking exploratory behavior. We further demonstrate, through controlled ablations, that emergent exploration vanishes if either environmental structure or agent memory is absent (Conditions 1 & 2). Surprisingly, removing long-horizon credit assignment (Condition 3) does not always prevent emergent exploration-a result we attribute to the pseudo-Thompson Sampling effect. These findings suggest that, under the right prerequisites, exploration and exploitation need not be treated as orthogonal objectives but can emerge from a unified reward-maximization process.

Problem

• Exploring novel environments with meta-RL agents
• Emergent exploration from greedy-only training
• Prerequisites for exploration in exploitation-focused agents

Innovation

• Greedy objective induces emergent exploration
• Recurring structure and memory enable exploration
• Long-horizon credit assignment not always needed