Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2006.07169v4
• Date: Wed, 19 May 2021 11:13:46 GMT
• Title: Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
• Authors: Filippos Christianos, Lukas Sch\"afer, Stefano V. Albrecht
• Abstract summary: Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms.
• Score: 11.292086312664383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.

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)
• MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure [37.56309011441144]
  This paper introduces MESA, a novel meta-exploration method for cooperative multi-agent learning. It learns to explore by first identifying the agents' high-rewarding joint state-action subspace from training tasks and then learning a set of diverse exploration policies to "cover" the subspace. Experiments show that with learned exploration policies, MESA achieves significantly better performance in sparse-reward tasks in several multi-agent particle environments and multi-agent MuJoCo environments.
  arXiv  Detail & Related papers  (2024-05-01T23:19:48Z)
• Sharing Knowledge in Multi-Task Deep Reinforcement Learning [57.38874587065694]
  We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We prove this by providing theoretical guarantees that highlight the conditions for which is convenient to share representations among tasks.
  arXiv  Detail & Related papers  (2024-01-17T19:31:21Z)
• Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
  This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
  arXiv  Detail & Related papers  (2023-03-24T15:12:28Z)
• Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning [15.195932300563541]
  This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning. We provide principled algorithms that incorporate a set of advisors by both evaluating the advisors at each state and subsequently using the advisors to guide action selection.
  arXiv  Detail & Related papers  (2023-01-26T15:00:23Z)
• Cooperative Exploration for Multi-Agent Deep Reinforcement Learning [127.4746863307944]
  We propose cooperative multi-agent exploration (CMAE) for deep reinforcement learning. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. We demonstrate that CMAE consistently outperforms baselines on various tasks.
  arXiv  Detail & Related papers  (2021-07-23T20:06:32Z)
• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning [20.901606233349177]
  Deep multi-agent reinforcement learning has shown the promise to solve complex cooperative tasks. In this paper, we aim to introduce diversity in both optimization and representation of shared multi-agent reinforcement learning. Our method achieves state-of-the-art performance on Google Research Football and super hard StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-06-04T00:55:03Z)
• 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)
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks [11.480994804659908]
  Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria. We provide a systematic evaluation and comparison of three different classes of MARL algorithms. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks.
  arXiv  Detail & Related papers  (2020-06-14T11:22:53Z)
• Non-local Policy Optimization via Diversity-regularized Collaborative Exploration [45.997521480637836]
  We propose a novel non-local policy optimization framework called Diversity-regularized Collaborative Exploration (DiCE) DiCE utilizes a group of heterogeneous agents to explore the environment simultaneously and share the collected experiences. We implement the framework in both on-policy and off-policy settings and the experimental results show that DiCE can achieve substantial improvement over the baselines.
  arXiv  Detail & Related papers  (2020-06-14T03:31:11Z)
• Never Give Up: Learning Directed Exploration Strategies [63.19616370038824]
  We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies. A self-supervised inverse dynamics model is used to train the embeddings of the nearest neighbour lookup, biasing the novelty signal towards what the agent can control.
  arXiv  Detail & Related papers  (2020-02-14T13:57:22Z)

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.