The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards

• URL: http://arxiv.org/abs/2401.05710v2
• Date: Wed, 18 Dec 2024 07:04:12 GMT
• Title: The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
• Authors: Xi Chen, Zhihui Zhu, Andrew Perrault,
• Abstract summary: The reward signal plays a central role in defining the desired behaviors of agents in reinforcement learning. We introduce a distributional reward critic framework for estimating reward distributions and perturbations during training. Our results broaden and deepen our ability to perform RL in reward-perturbed environments.
• Score: 31.550669983576544
• License:
• Abstract: The reward signal plays a central role in defining the desired behaviors of agents in reinforcement learning (RL). Rewards collected from realistic environments could be perturbed, corrupted, or noisy due to an adversary, sensor error, or because they come from subjective human feedback. Thus, it is important to construct agents that can learn under such rewards. Existing methodologies for this problem make strong assumptions, including that the perturbation is known in advance, clean rewards are accessible, or that the perturbation preserves the optimal policy. We study a new, more general, class of unknown perturbations, and introduce a distributional reward critic framework for estimating reward distributions and perturbations during training. Our proposed methods are compatible with any RL algorithm. Despite their increased generality, we show that they achieve comparable or better rewards than existing methods in a variety of environments, including those with clean rewards. Under the challenging and generalized perturbations we study, we win/tie the highest return in 44/48 tested settings (compared to 11/48 for the best baseline). Our results broaden and deepen our ability to perform RL in reward-perturbed environments.

Related papers

• The Dark Side of Rich Rewards: Understanding and Mitigating Noise in VLM Rewards [31.806143589311652]
  Vision-Language Models (VLMs) are increasingly used to generate reward signals for training embodied agents. Our research reveals that agents guided by VLM rewards often underperform compared to those employing only intrinsic rewards. We introduce BiMI, a novel reward function designed to mitigate noise.
  arXiv  Detail & Related papers  (2024-09-24T09:45:20Z)
• Random Latent Exploration for Deep Reinforcement Learning [71.88709402926415]
  This paper introduces a new exploration technique called Random Latent Exploration (RLE) RLE combines the strengths of bonus-based and noise-based (two popular approaches for effective exploration in deep RL) exploration strategies. We evaluate it on the challenging Atari and IsaacGym benchmarks and show that RLE exhibits higher overall scores across all the tasks than other approaches.
  arXiv  Detail & Related papers  (2024-07-18T17:55:22Z)
• Reinforcement Learning from Bagged Reward [46.16904382582698]
  In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent. In many real-world scenarios, designing immediate reward signals is difficult. We propose a novel reward redistribution method equipped with a bidirectional attention mechanism.
  arXiv  Detail & Related papers  (2024-02-06T07:26:44Z)
• 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)
• A Multiplicative Value Function for Safe and Efficient Reinforcement Learning [131.96501469927733]
  We propose a safe model-free RL algorithm with a novel multiplicative value function consisting of a safety critic and a reward critic. The safety critic predicts the probability of constraint violation and discounts the reward critic that only estimates constraint-free returns. We evaluate our method in four safety-focused environments, including classical RL benchmarks augmented with safety constraints and robot navigation tasks with images and raw Lidar scans as observations.
  arXiv  Detail & Related papers  (2023-03-07T18:29:15Z)
• Internally Rewarded Reinforcement Learning [22.01249652558878]
  We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model. We show that the proposed reward function can consistently stabilize the training process by reducing the impact of reward noise.
  arXiv  Detail & Related papers  (2023-02-01T06:25:46Z)
• Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning [19.788336796981685]
  We propose a novel Distributional Reward Estimation framework for effective Multi-Agent Reinforcement Learning (DRE-MARL) Our main idea is to design the multi-action-branch reward estimation and policy-weighted reward aggregation for stabilized training. The superiority of the DRE-MARL is demonstrated using benchmark multi-agent scenarios, compared with the SOTA baselines in terms of both effectiveness and robustness.
  arXiv  Detail & Related papers  (2022-10-14T08:31:45Z)
• 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)
• Unbiased Methods for Multi-Goal Reinforcement Learning [13.807859854345834]
  In multi-goal reinforcement learning, the reward for each goal is sparse, and located in a small neighborhood of the goal. We show that Hindsight Experience Replay (HER) can converge to low-return policies by overestimating chancy outcomes. We introduce unbiased deep Q-learning and actor-critic algorithms that can handle such infinitely sparse rewards, and test them in toy environments.
  arXiv  Detail & Related papers  (2021-06-16T15:31:51Z)
• Semi-supervised reward learning for offline reinforcement learning [71.6909757718301]
  Training agents usually requires reward functions, but rewards are seldom available in practice and their engineering is challenging and laborious. We propose semi-supervised learning algorithms that learn from limited annotations and incorporate unlabelled data. In our experiments with a simulated robotic arm, we greatly improve upon behavioural cloning and closely approach the performance achieved with ground truth rewards.
  arXiv  Detail & Related papers  (2020-12-12T20:06:15Z)
• Maximizing Information Gain in Partially Observable Environments via Prediction Reward [64.24528565312463]
  This paper tackles the challenge of using belief-based rewards for a deep RL agent. We derive the exact error between negative entropy and the expected prediction reward. This insight provides theoretical motivation for several fields using prediction rewards.
  arXiv  Detail & Related papers  (2020-05-11T08:13:49Z)

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.