Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification

• URL: http://arxiv.org/abs/2308.15969v1
• Date: Wed, 30 Aug 2023 11:45:40 GMT
• Title: Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
• Authors: Jasmina Gajcin, James McCarthy, Rahul Nair, Radu Marinescu, Elizabeth Daly, Ivana Dusparic
• Abstract summary: ITERS is an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions.
• Score: 15.453123084827089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging task, especially in complex, multi-objective environments. Developers often have to resort to starting with an initial, potentially misspecified reward function, and iteratively adjusting its parameters, based on observed learned behavior. In this work, we aim to automate this process by proposing ITERS, an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration. We also allow the user to provide explanations of their feedback, which are used to augment the feedback and reduce user effort and feedback frequency. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions.

Related papers

• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe is a novel trainer-student system that learns a dynamic reward function based on the student's performance and alignment with expert demonstrations. RILe enables better performance in complex settings where traditional methods falter, outperforming existing methods by 2x in complex simulated robot-locomotion tasks.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• REBEL: A Regularization-Based Solution for Reward Overoptimization in Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
  A misalignment between the reward function and user intentions, values, or social norms can be catastrophic in the real world. Current methods to mitigate this misalignment work 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)
• Behavior Alignment via Reward Function Optimization [23.92721220310242]
  We introduce a new framework that integrates auxiliary rewards reflecting a designer's domain knowledge with the environment's primary rewards. We evaluate our method's efficacy on a diverse set of tasks, from small-scale experiments to high-dimensional control challenges.
  arXiv  Detail & Related papers  (2023-10-29T13:45:07Z)
• A State Augmentation based approach to Reinforcement Learning from Human Preferences [20.13307800821161]
  Preference Based Reinforcement Learning attempts to solve the issue by utilizing binary feedbacks on queried trajectory pairs. We present a state augmentation technique that allows the agent's reward model to be robust.
  arXiv  Detail & Related papers  (2023-02-17T07:10:50Z)
• Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience [89.30876995059168]
  inverse reinforcement learning (IRL) -- inferring the reward function of an agent from observing its behavior. This paper addresses the problem of IRL -- inferring the reward function of an agent from observing its behavior.
  arXiv  Detail & Related papers  (2022-08-09T17:29:49Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference [71.11416263370823]
  We propose a generative inverse reinforcement learning for user behavioral preference modelling. Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
  arXiv  Detail & Related papers  (2021-05-03T13:14:25Z)
• Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learning [2.3226893628361682]
  In this paper, we focus on training a single agent to score goals with binary success/failure reward function. The proposed method can be utilized to train agents in environments with fairly complex state and action spaces.
  arXiv  Detail & Related papers  (2021-05-02T16:01:34Z)
• Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping [71.214923471669]
  Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL) In this paper, we consider the problem of adaptively utilizing a given shaping reward function. Experiments in sparse-reward cartpole and MuJoCo environments show that our algorithms can fully exploit beneficial shaping rewards.
  arXiv  Detail & Related papers  (2020-11-05T05:34:14Z)

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.