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Found 3 papers, 2 papers with code
Direct Preference-based Policy Optimization without Reward Modeling
We apply our algorithm to offline RL tasks with actual human preference labels and show that our algorithm outperforms or is on par with the existing PbRL methods.
Know Your Action Set: Learning Action Relations for Reinforcement Learning
Intelligent agents can solve tasks in a variety of ways depending on the action set at their disposal.
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
In this paper, we extend VI-MPC and PaETS, which have been originally introduced in previous literature, to address partially observable cases.
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