# Hill Climbing on Value Estimates for Search-control in Dyna

arXiv: https://arxiv.org/abs/1906.07791 · HTML: https://mlsanity.com/paper/1906.07791/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization](https://mlsanity.com/paper/2301.07784/) (2023)
- [MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL](https://mlsanity.com/paper/2410.08896/) (2024)
- [Goal-Space Planning with Subgoal Models](https://mlsanity.com/paper/2206.02902/) (2022)
- [Locality Sensitive Sparse Encoding for Learning World Models Online](https://mlsanity.com/paper/2401.13034/) (2024)
- [Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning](https://mlsanity.com/paper/2602.12375/) (2026)
- [Planning with Expectation Models for Control](https://mlsanity.com/paper/2104.08543/) (2021)

## Abstract (arXiv)

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