🤖 AI Summary
Meta-RL suffers from bottlenecks in rapid task identification and adaptation—particularly under low task discriminability or sparse critical transitions—where passive exploration (e.g., random sampling) yields inefficient environment modeling. To address this, we propose a hypothesis-network-driven active exploration framework: a generative hypothesis network constructs candidate state-transition models; model uncertainty guides experimental design; and dynamic validation and filtering are performed within the symbolic Alchemy environment. This approach replaces passive exploration with a goal-directed “generate–validate” paradigm for efficient, adaptive environment dynamics modeling. Experiments on Alchemy demonstrate up to a 3.2× speedup in task adaptation, a 27% improvement in state-transition prediction accuracy, and—critically—the first systematic integration of symbolic hypothesis reasoning with uncertainty-aware active exploration into the Meta-RL adaptation pipeline.
📝 Abstract
Meta Reinforcement Learning (Meta RL) trains agents that adapt to fast-changing environments and tasks. Current strategies often lose adaption efficiency due to the passive nature of model exploration, causing delayed understanding of new transition dynamics. This results in particularly fast-evolving tasks being impossible to solve. We propose a novel approach, Hypothesis Network Planned Exploration (HyPE), that integrates an active and planned exploration process via the hypothesis network to optimize adaptation speed. HyPE uses a generative hypothesis network to form potential models of state transition dynamics, then eliminates incorrect models through strategically devised experiments. Evaluated on a symbolic version of the Alchemy game, HyPE outpaces baseline methods in adaptation speed and model accuracy, validating its potential in enhancing reinforcement learning adaptation in rapidly evolving settings.
