Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2404.09715v1
• Date: Mon, 15 Apr 2024 12:18:09 GMT
• Title: Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
• Authors: Linjie Xu, Zichuan Liu, Alexander Dockhorn, Diego Perez-Liebana, Jinyu Wang, Lei Song, Jiang Bian,
• Abstract summary: The sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability, non-stationary training, and enormous strategy space. We argue that the widely used episodic training mechanism could be a source of poor sample efficiency. To better exploit the data already collected, we propose to increase the frequency of the gradient updates per environment interaction.
• Score: 47.17030172520195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability, non-stationary training, and enormous strategy space. Although much effort has been devoted to developing new methods and enhancing sample efficiency, we look at the widely used episodic training mechanism. In each training step, tens of frames are collected, but only one gradient step is made. We argue that this episodic training could be a source of poor sample efficiency. To better exploit the data already collected, we propose to increase the frequency of the gradient updates per environment interaction (a.k.a. Replay Ratio or Update-To-Data ratio). To show its generality, we evaluate $3$ MARL methods on $6$ SMAC tasks. The empirical results validate that a higher replay ratio significantly improves the sample efficiency for MARL algorithms. The codes to reimplement the results presented in this paper are open-sourced at https://anonymous.4open.science/r/rr_for_MARL-0D83/.

Related papers

• 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)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)
• Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning [7.07264650720021]
  Sub-optimal Data Pre-training, SDP, is an approach that leverages reward-free, sub-optimal data to improve HitL RL algorithms. We show SDP can significantly improve or achieve competitive performance with state-of-the-art HitL RL algorithms.
  arXiv  Detail & Related papers  (2024-04-30T18:58:33Z)
• MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning [128.19212716007794]
  We propose an effective framework called textbfMulti-textbfAgent textbfMasked textbfAttentive textbfContrastive textbfLearning (MA2CL) MA2CL encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios.
  arXiv  Detail & Related papers  (2023-06-03T05:32:19Z)
• Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse [28.29966904455002]
  We propose to train an off-policy RL agent via updating on a fixed sampled batch multiple times. We name our method sample multiple reuse (SMR) and show the properties of Q-learning with SMR. SMR significantly boosts the sample efficiency of the base methods across most of the evaluated tasks.
  arXiv  Detail & Related papers  (2023-05-29T03:25:22Z)
• A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection [0.4893345190925178]
  We propose detecting and removing hard samples directly from the training procedure rather than applying complicated algorithms to mitigate their effects. Our results on SVHN and CIFAR-10 datasets show the effectiveness of this method in improving the adversarial training without adding too much computational cost.
  arXiv  Detail & Related papers  (2023-01-25T08:13:50Z)
• ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data [93.06336507035486]
  Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. We present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks.
  arXiv  Detail & Related papers  (2022-10-27T02:55:15Z)
• CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning [56.20123080771364]
  We develop a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF) for reinforcement learning. CCLF fully exploit sample importance and improve learning efficiency in a self-supervised manner. We evaluate this approach on the DeepMind Control Suite, Atari, and MiniGrid benchmarks.
  arXiv  Detail & Related papers  (2022-05-02T14:42:05Z)
• Learning Fast Sample Re-weighting Without Reward Data [41.92662851886547]
  This paper presents a novel learning-based fast sample re-weighting (FSR) method that does not require additional reward data. Our experiments show the proposed method achieves competitive results compared to state of the arts on label noise and long-tailed recognition.
  arXiv  Detail & Related papers  (2021-09-07T17:30:56Z)
• Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations [55.41644538483948]
  In this paper, we propose a combination of approaches that allow the agent to use low-quality demonstrations in complex vision-based environments. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.
  arXiv  Detail & Related papers  (2020-06-17T15:38:40Z)

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.