  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.
