Performance Optimization of Ratings-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2501.07755v1
• Date: Mon, 13 Jan 2025 23:56:24 GMT
• Title: Performance Optimization of Ratings-Based Reinforcement Learning
• Authors: Evelyn Rose, Devin White, Mingkang Wu, Vernon Lawhern, Nicholas R. Waytowich, Yongcan Cao,
• Abstract summary: This paper explores multiple optimization methods to improve the performance of rating-based reinforcement learning (RbRL) RbRL has been developed to infer reward functions in reward-free environments for the subsequent policy learning via standard reinforcement learning.
• Score: 1.6133809033337525
• License:
• Abstract: This paper explores multiple optimization methods to improve the performance of rating-based reinforcement learning (RbRL). RbRL, a method based on the idea of human ratings, has been developed to infer reward functions in reward-free environments for the subsequent policy learning via standard reinforcement learning, which requires the availability of reward functions. Specifically, RbRL minimizes the cross entropy loss that quantifies the differences between human ratings and estimated ratings derived from the inferred reward. Hence, a low loss means a high degree of consistency between human ratings and estimated ratings. Despite its simple form, RbRL has various hyperparameters and can be sensitive to various factors. Therefore, it is critical to provide comprehensive experiments to understand the impact of various hyperparameters on the performance of RbRL. This paper is a work in progress, providing users some general guidelines on how to select hyperparameters in RbRL.

Related papers

• RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning [1.7095639309883044]
  Reinforcement learning (RL) learns policies from various experiences based on the associated cumulative return/rewards without treating them differently. This paper proposes a novel RL method that mimics humans' decision making process by differentiating among collected experiences for effective policy learning.
  arXiv  Detail & Related papers  (2025-01-13T17:19:34Z)
• Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach [0.9549646359252346]
  In deep Reinforcement Learning (RL) models trained using gradient-based techniques, the choice of gradient and its learning rate are crucial to achieving good performance. We propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta-learning approach that selects the learning rate based on the agent's performance during training.
  arXiv  Detail & Related papers  (2024-10-16T14:15:28Z)
• Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation [37.36913210031282]
  Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering. We propose SEER, an efficient PbRL method that integrates label smoothing and policy regularization techniques.
  arXiv  Detail & Related papers  (2024-05-29T01:49:20Z)
• HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback [47.12549302721597]
  We propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF) This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses.
  arXiv  Detail & Related papers  (2024-03-13T07:38:20Z)
• RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences [23.414135977983953]
  Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engineering by harnessing human preferences as the reward signal. We present RIME, a robust PbRL algorithm for effective reward learning from noisy preferences.
  arXiv  Detail & Related papers  (2024-02-27T07:03:25Z)
• REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
  A misalignment between the reward function and human preferences can lead to catastrophic outcomes in the real world. Recent methods aim to mitigate misalignment by learning reward functions from human preferences. We propose a novel concept of reward regularization within the robotic RLHF framework.
  arXiv  Detail & Related papers  (2023-12-22T04:56:37Z)
• Provable Reward-Agnostic Preference-Based Reinforcement Learning [61.39541986848391]
  Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories. We propose a theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired.
  arXiv  Detail & Related papers  (2023-05-29T15:00:09Z)
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning [88.34958680436552]
  We present an exploration method specifically for preference-based reinforcement learning algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms.
  arXiv  Detail & Related papers  (2022-05-24T23:22:10Z)
• Self-supervised Representation Learning with Relative Predictive Coding [102.93854542031396]
  Relative Predictive Coding (RPC) is a new contrastive representation learning objective. RPC maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance. We empirically verify the effectiveness of RPC on benchmark vision and speech self-supervised learning tasks.
  arXiv  Detail & Related papers  (2021-03-21T01:04:24Z)
• Information Directed Reward Learning for Reinforcement Learning [64.33774245655401]
  We learn a model of the reward function that allows standard RL algorithms to achieve high expected return with as few expert queries as possible. In contrast to prior active reward learning methods designed for specific types of queries, IDRL naturally accommodates different query types. We support our findings with extensive evaluations in multiple environments and with different types of queries.
  arXiv  Detail & Related papers  (2021-02-24T18:46:42Z)
• Kalman meets Bellman: Improving Policy Evaluation through Value Tracking [59.691919635037216]
  Policy evaluation is a key process in Reinforcement Learning (RL) We devise an optimization method, called Kalman Optimization for Value Approximation (KOVA) KOVA minimizes a regularized objective function that concerns both parameter and noisy return uncertainties.
  arXiv  Detail & Related papers  (2020-02-17T13:30:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.