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Dyna is an architecture for model-based reinforcement learning (RL), wheresimulated experience from a model is used to update policies or valuefunctions. A key component of Dyna is search-control, the mechanism to generatethe state and action from which the agent queries the model, which remainslargely unexplored. In this work, we propose to generate such states by usingthe trajectory obtained from Hill Climbing (HC) the current estimate of thevalue function. This has the effect of propagating value from high-valueregions and of preemptively updating value estimates of the regions that theagent is likely to visit next. We derive a noisy projected natural gradientalgorithm for hill climbing, and highlight a connection to Langevin dynamics.We provide an empirical demonstration on four classical domains that ouralgorithm, HC-Dyna, can obtain significant sample efficiency improvements. Westudy the properties of different sampling distributions for search-control,and find that there appears to be a benefit specifically from using the samplesgenerated by climbing on current value estimates from low-value to high-valueregion.

Hill Climbing on Value Estimates for Search-control in Dyna