Reward Prediction Error Prioritisation in Experience Replay: The RPE-PER Method

• URL: http://arxiv.org/abs/2501.18093v1
• Date: Thu, 30 Jan 2025 02:09:35 GMT
• Title: Reward Prediction Error Prioritisation in Experience Replay: The RPE-PER Method
• Authors: Hoda Yamani, Yuning Xing, Lee Violet C. Ong, Bruce A. MacDonald, Henry Williams,
• Abstract summary: We introduce Reward Predictive Error Prioritised Experience Replay (RPE-PER) RPE-PER prioritises experiences in the buffer based on RPEs. Our method employs a critic network, EMCN, that predicts rewards in addition to the Q-values produced by standard critic networks.
• Score: 1.600323605807673
• License:
• Abstract: Reinforcement Learning algorithms aim to learn optimal control strategies through iterative interactions with an environment. A critical element in this process is the experience replay buffer, which stores past experiences, allowing the algorithm to learn from a diverse range of interactions rather than just the most recent ones. This buffer is especially essential in dynamic environments with limited experiences. However, efficiently selecting high-value experiences to accelerate training remains a challenge. Drawing inspiration from the role of reward prediction errors (RPEs) in biological systems, where they are essential for adaptive behaviour and learning, we introduce Reward Predictive Error Prioritised Experience Replay (RPE-PER). This novel approach prioritises experiences in the buffer based on RPEs. Our method employs a critic network, EMCN, that predicts rewards in addition to the Q-values produced by standard critic networks. The discrepancy between these predicted and actual rewards is computed as RPE and utilised as a signal for experience prioritisation. Experimental evaluations across various continuous control tasks demonstrate RPE-PER's effectiveness in enhancing the learning speed and performance of off-policy actor-critic algorithms compared to baseline approaches.

Related papers

• CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing [70.25689961697523]
  We propose a generalizable algorithm that enhances sequential reasoning by cross-task experience sharing and selection. Our work bridges the gap between existing sequential reasoning paradigms and validates the effectiveness of leveraging cross-task experiences.
  arXiv  Detail & Related papers  (2024-10-22T03:59:53Z)
• Directly Attention Loss Adjusted Prioritized Experience Replay [0.07366405857677226]
  Prioritized Replay Experience (PER) enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. DALAP is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network.
  arXiv  Detail & Related papers  (2023-11-24T10:14:05Z)
• Attention Loss Adjusted Prioritized Experience Replay [0.0]
  Prioritized Replay Experience (PER) is a technical means of deep reinforcement learning by selecting experience samples with more knowledge quantity to improve the training rate of neural network. Non-uniform sampling used in PER inevitably shifts the state-action space distribution and brings the estimation error of Q-value function. An Attention Loss Adjusted Prioritized (ALAP) Experience Replay algorithm is proposed, which integrates the improved Self-Attention network with Double-Sampling mechanism.
  arXiv  Detail & Related papers  (2023-09-13T02:49:32Z)
• Actor Prioritized Experience Replay [0.0]
  Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error. We introduce a novel experience replay sampling framework for actor-critic methods, which also regards issues with stability and recent findings behind the poor empirical performance of PER. An extensive set of experiments verifies our theoretical claims and demonstrates that the introduced method significantly outperforms the competing approaches.
  arXiv  Detail & Related papers  (2022-09-01T15:27:46Z)
• 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)
• Variance Reduction based Experience Replay for Policy Optimization [3.0790370651488983]
  Variance Reduction Experience Replay (VRER) is a framework for the selective reuse of relevant samples to improve policy gradient estimation. VRER forms the foundation of our sample efficient off-policy learning algorithm known as Policy Gradient with VRER.
  arXiv  Detail & Related papers  (2021-10-17T19:28:45Z)
• Improving Music Performance Assessment with Contrastive Learning [78.8942067357231]
  This study investigates contrastive learning as a potential method to improve existing MPA systems. We introduce a weighted contrastive loss suitable for regression tasks applied to a convolutional neural network. Our results show that contrastive-based methods are able to match and exceed SoTA performance for MPA regression tasks.
  arXiv  Detail & Related papers  (2021-08-03T19:24:25Z)
• Learning to Sample with Local and Global Contexts in Experience Replay Buffer [135.94190624087355]
  We propose a new learning-based sampling method that can compute the relative importance of transition. We show that our framework can significantly improve the performance of various off-policy reinforcement learning methods.
  arXiv  Detail & Related papers  (2020-07-14T21:12:56Z)
• Revisiting Fundamentals of Experience Replay [91.24213515992595]
  We present a systematic and extensive analysis of experience replay in Q-learning methods. We focus on two fundamental properties: the replay capacity and the ratio of learning updates to experience collected.
  arXiv  Detail & Related papers  (2020-07-13T21:22:17Z)
• Experience Replay with Likelihood-free Importance Weights [123.52005591531194]
  We propose to reweight experiences based on their likelihood under the stationary distribution of the current policy. We apply the proposed approach empirically on two competitive methods, Soft Actor Critic (SAC) and Twin Delayed Deep Deterministic policy gradient (TD3)
  arXiv  Detail & Related papers  (2020-06-23T17:17:44Z)

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.