inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulatorfree direct policy learning, calledPreference − basedPolicyLearning(PPL). PPL iterates a four-step process: the robot demonstrates a candidate policy; the expert ranks this...
Many machine learning approaches in robotics, based on re- inforcement learning, inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulator- free direct policy learning, called Preference-based Policy Learning (PPL). PPL iterates a four...
(2011). Preference-based policy iteration: Leveraging preference learning for reinforcement learning. In Proceedings ECMLPKDD 2011, European conference on machine learning and principles and practice of knowledge discovery in databases (pp. 414-429). Berlin: Springer....
考虑到在这个过程中奖励函数会在训练过程中剧烈变化,PrefPPO (NIPS2017, OpenAI & DeepMind)使用了on-policy的PPO算法来规避训练的不稳定问题。 PEBBLE: unsupervised PrEtraining and preference-Based learning via relaBeLing Experience 通过上述过程,我们已经可以形成完整的Preference-based RL训练框架,但是也会带来low ...
A policy iteration algorithm for learning from preference-based feedback - Wirth, Fürnkranz - 2013 () Citation Context ... each preference encountered, requiring a high amount of evaluations for convergence. 5.2 A Policy Iteration Algorithm for Learning from Preference-based Feedback The ...
Sebag. Preference-based policy learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 12– 27. Springer, 2011. 2 [2] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. InfoGAN: Interpretable representation learning by...
Official implementation of "Direct Preference-based Policy Optimization without Reward Modeling" (NeurIPS 2023) Topicsreinforcement-learning offline-reinforcement-learning rlhf preference-based-reinforcement-learning ResourcesReadme LicenseMIT license Activity ...
Policy-based Agent Directability Many potential applications for agent technology require humans and agents to work together to achieve complex tasks effectively. In contrast, most of the ... KL Myers,DN Morley - Springer US 被引量: 43发表: 2003年 Preference-based Health status in a German out...
Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning 4 Jan 2024 · Ke Li, Han Guo · Edit social preview Multi-objective reinforcement learning (MORL) aims to find a set of high-performing and diverse policies that address trade-offs between multiple ...
Lastly, we numerically compare the greedy entropy reduction policy with a knowledge gradient policy under a number of scenarios, examining their performance under both differential entropy and misclassification error. 展开 关键词: Statistics - Machine Learning Computer Science - Information Theory Computer ...