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-objective applications, representation learning arises as an effective technique to deal with the curse of dimensionality in reward-free RL...
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 ...
Reinforcement Learning (RL) problems are being considered under increasingly more complex structures. While tabular and linear models have been thoroughly explored, the analytical study of RL under nonlinear function approximation, especially kernel-based models, has recently gained traction for their ...
2、Reward-free setting 文章提出的这个范式,在第一个探索阶段只做 reward-free 的探索,这个交互和标准的 RL 交互的区别就在于环境不返回奖励。相比于标准 RL,其他方面都一样,比如都具有一个固定的初始状态分布,并且要从该分布出发根据 transition dynamics 来访问各个状态。 3、Overview 其中,第 1、2 步就是我们...
During RL, we need to evaluate the agent A many times. If we want to use a learned reward function we may need to evaluate A more times. And if we want to train a policy which remains benign off of the training distribution, we may need to evaluate A more times (e.g. since we ...
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...
Deep RL Reward Function Design for Lane-Free Autonomous DrivingIn this paper we present an application of Deep Reinforcement Learning to lane-free traffic, where vehicles do not adhere to the notion of lanes, but are rather able to be located at any lateral position within the road boundaries....
We found that using a strong reward model for annotating preference optimization datasets is crucial. In this iteration, we have reannotated the datasetprinceton-nlp/llama3-ultrafeedback-armormusing a more powerful reward model,RLHFlow/ArmoRM-Llama3-8B-v0.1. As a result, the v0.2 models demon...
Reinforcement learning (RL) techniques are a set of solutions for optimal long-term action choice such that actions take into account both immediate and delayed consequences. They fall into two broad classes: model-based and model-free approaches. Model-based approaches assume an explicit model of...
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 ...