讲者: Yingbin Liang Professor at the Department of Electrical and Computer Engineering at the Ohio State University (OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-...
In this post I argue that these three pieces may be sufficient to get a benign and competitive version of model-free reinforcement learning. I think this is an important intermediate goal of solving AI control. This post doesn’t discuss benign model-based RL at all, which I think is anoth...
DPO 对 RLHF 中的奖励函数进行了重新参数化,以便直接从偏好数据中学习策略模型,从而消除了对显式奖励模型的需求。由于其简单性和稳定性,它已获得广泛的实际采用。在 DPO 中,隐式奖励是使用当前策略模型和监督微调 (SFT) 模型之间响应可能性的对数比来表示的。然而,这种奖励公式并不直接与用于指导生成的指标相一致...
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Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To isolate the challenges of exploration, we propose a new "reward-free RL" framework. In the exploration phase, the agent ...
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We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in two phases. In the exploration phase, the agent interacts with the environment and collects samples without the reward. ...
In this context, we examine the question of statistical efficiency in kernel-based RL within the reward-free RL framework, specifically asking: how many samples are required to design a near-optimal policy? Existing work addresses this question under restrictive assumptions about the class of kernel...
In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation ...
Recipes to train reward model for RLHF. Contribute to RLHFlow/RLHF-Reward-Modeling development by creating an account on GitHub.