Multi-Agent Transfer Learning via Temporal Contrastive Learning

• URL: http://arxiv.org/abs/2406.01377v1
• Date: Mon, 3 Jun 2024 14:42:14 GMT
• Title: Multi-Agent Transfer Learning via Temporal Contrastive Learning
• Authors: Weihao Zeng, Joseph Campbell, Simon Stepputtis, Katia Sycara,
• Abstract summary: This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals.
• Score: 8.487274986507922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals. The approach involves pre-training a goal-conditioned agent, finetuning it on the target domain, and using contrastive learning to construct a planning graph that guides the agent via sub-goals. Experiments on multi-agent coordination Overcooked tasks demonstrate improved sample efficiency, the ability to solve sparse-reward and long-horizon problems, and enhanced interpretability compared to baselines. The results highlight the effectiveness of integrating goal-conditioned policies with unsupervised temporal abstraction learning for complex multi-agent transfer learning. Compared to state-of-the-art baselines, our method achieves the same or better performances while requiring only 21.7% of the training samples.

Related papers

• Online inductive learning from answer sets for efficient reinforcement learning exploration [52.03682298194168]
  We exploit inductive learning of answer set programs to learn a set of logical rules representing an explainable approximation of the agent policy. We then perform answer set reasoning on the learned rules to guide the exploration of the learning agent at the next batch. Our methodology produces a significant boost in the discounted return achieved by the agent, even in the first batches of training.
  arXiv  Detail & Related papers  (2025-01-13T16:13:22Z)
• 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)
• Transfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping [9.81076530822611]
  We propose a method that learns a subgoal mapping between the expert agent policy and the learner agent policy. We learn this subgoal mapping by training a Long Short Term Memory (LSTM) network for a distribution of tasks. We demonstrate that the proposed learning scheme can effectively find the subgoal mapping underlying the given distribution of tasks.
  arXiv  Detail & Related papers  (2024-10-18T14:08:41Z)
• Variational Offline Multi-agent Skill Discovery [43.869625428099425]
  We propose two novel auto-encoder schemes to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills. Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining.
  arXiv  Detail & Related papers  (2024-05-26T00:24:46Z)
• MADiff: Offline Multi-agent Learning with Diffusion Models [79.18130544233794]
  MADiff is a diffusion-based multi-agent learning framework. It works as both a decentralized policy and a centralized controller. Our experiments demonstrate that MADiff outperforms baseline algorithms across various multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:14:09Z)
• Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation [13.670618752160594]
  Deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. Traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. We propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search.
  arXiv  Detail & Related papers  (2022-06-25T19:09:29Z)
• SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments [12.959163198988536]
  Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. A new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents.
  arXiv  Detail & Related papers  (2021-07-01T08:15:05Z)
• Importance Weighted Policy Learning and Adaptation [89.46467771037054]
  We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
  arXiv  Detail & Related papers  (2020-09-10T14:16:58Z)
• Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
  Continually solving new, unsolved tasks is the key to learning diverse behaviors. In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. We propose setting up an automatic curriculum for goals that the agent needs to solve. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-17T03:58:25Z)
• Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling [126.69933134648541]
  We present a meta-reinforcement learning algorithm that is both efficient and extrapolates well when faced with out-of-distribution tasks at test time. Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data.
  arXiv  Detail & Related papers  (2020-06-12T13:34:46Z)

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.