Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2211.15612v1
• Date: Mon, 28 Nov 2022 18:11:26 GMT
• Title: Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning
• Authors: Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang
• Abstract summary: offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets. One agent learned by offline MARL often inherits this random policy, jeopardizing the performance of the entire team. We propose a novel framework called Shared Individual Trajectories (SIT) to address this problem.
• Score: 98.07495732562654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of multi-agent systems in real-world applications. However, in practice, each individual behavior policy that generates multi-agent joint trajectories usually has a different level of how well it performs. e.g., an agent is a random policy while other agents are medium policies. In the cooperative game with global reward, one agent learned by existing offline MARL often inherits this random policy, jeopardizing the performance of the entire team. In this paper, we investigate offline MARL with explicit consideration on the diversity of agent-wise trajectories and propose a novel framework called Shared Individual Trajectories (SIT) to address this problem. Specifically, an attention-based reward decomposition network assigns the credit to each agent through a differentiable key-value memory mechanism in an offline manner. These decomposed credits are then used to reconstruct the joint offline datasets into prioritized experience replay with individual trajectories, thereafter agents can share their good trajectories and conservatively train their policies with a graph attention network (GAT) based critic. We evaluate our method in both discrete control (i.e., StarCraft II and multi-agent particle environment) and continuous control (i.e, multi-agent mujoco). The results indicate that our method achieves significantly better results in complex and mixed offline multi-agent datasets, especially when the difference of data quality between individual trajectories is large.

Related papers

• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments [3.0284592792243794]
  Bottom Up Network (BUN) treats the collective of multi-agents as a unified entity. Our empirical evaluations across a variety of cooperative multi-agent scenarios, including tasks such as cooperative navigation and traffic control, consistently demonstrate BUN's superiority over baseline methods with substantially reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-03T14:25:02Z)
• ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization [11.620274237352026]
  offline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets. MARL presents additional challenges due to the large joint state-action space and the complexity of multi-agent behaviors. We introduce a regularizer in the space of stationary distributions to better handle distributional shift.
  arXiv  Detail & Related papers  (2024-10-02T18:56:10Z)
• Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization [23.416448404647305]
  OMIGA is a new offline m ulti-agent RL algorithm with implicit global-to-local v alue regularization. We show that OMIGA achieves superior performance over the state-of-the-art offline MARL methods in almost all tasks.
  arXiv  Detail & Related papers  (2023-07-21T14:37:54Z)
• MADiff: Offline Multi-agent Learning with Diffusion Models [79.18130544233794]
  Diffusion model (DM) recently achieved huge success in various scenarios including offline reinforcement learning. We propose MADiff, a novel generative multi-agent learning framework to tackle this problem. Our experiments show the superior performance of MADiff compared to baseline algorithms in a wide range of multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:14:09Z)
• RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning [90.43925357575543]
  We propose ranked policy memory ( RPM) to collect diverse multi-agent trajectories for training MARL policies with good generalizability. RPM enables MARL agents to interact with unseen agents in multi-agent generalization evaluation scenarios and complete given tasks, and it significantly boosts the performance up to 402% on average.
  arXiv  Detail & Related papers  (2022-10-18T07:32:43Z)
• Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification [74.10976684469435]
  offline reinforcement learning (RL) algorithms can be transferred to multi-agent settings directly. We propose a simple yet effective method, Offline Multi-Agent RL with Actor Rectification (OMAR), to tackle this critical challenge. OMAR significantly outperforms strong baselines with state-of-the-art performance in multi-agent continuous control benchmarks.
  arXiv  Detail & Related papers  (2021-11-22T13:27:42Z)
• AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning [22.890835786710316]
  This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers. We exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy.
  arXiv  Detail & Related papers  (2021-05-10T08:39:56Z)
• Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic [54.2180984002807]
  Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems. We propose a multi-agent inverse RL algorithm that is more sample-efficient and scalable than previous works.
  arXiv  Detail & Related papers  (2020-02-24T20:30:45Z)

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.