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Source note2 min read

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

Source

arXiv

arxiv.org/abs/2606.24601v1

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Source

Plain-Language Brief

The paper introduces a new method called ASALT for multi-agent reinforcement learning (MARL). This method is designed to handle situations where the state-space dimensionalities between source and target domains don't match, which is a common constraint in existing MARL approaches. The proposed solution involves observation-level and state-level adapters that create shared embeddings to facilitate knowledge transfer across various actors and critics. Experimental results show that ASALT outperforms existing baselines in cooperative settings and reduces negative transfer, making it more efficient.

What the Document Says

  • The paper discusses a method called ASALT for multi-agent reinforcement learning (MARL).
  • ASALT handles mismatched state-space dimensionalities between source and target domains in MARL.
  • The method uses observation-level and state-level adapters to create shared embeddings.
  • These adapters enable efficient strategy transfer across heterogeneous domains.
  • Experimental results show that ASALT outperforms existing baselines in terms of sample efficiency and global return in cooperative settings.
  • The effectiveness of ASALT depends on the degree of mismatch between source and target domains.
  • ASALT mitigates negative transfer, which is a common issue when transferring policies between domains with differing observation and action spaces.

Key Concepts Mentioned

  • multi-agent reinforcement learning
  • ASALT
  • state-space dimensionalities
  • observation-level adapters
  • state-level adapters
  • shared embeddings
  • strategy transfer
  • heterogeneous domains
  • sample efficiency
  • global return
  • negative transfer

Claims Made by the Document

  • ASALT outperforms existing baselines.
  • ASALT mitigates negative transfer.

Evidence Used

  • Experimental results on multiple configurations in standard benchmark environments.

Methods / Approach

  • Multi-agent reinforcement learning. Adapters for observation and state level.
  • Creating shared embeddings.

Limitations Stated by the Authors

The effectiveness of ASALT depends on the degree of mismatch between source and target domains.

Limitations Noted by the Agent

Agent interpretation — not from the source.

Not stated in source

Open Questions

  • What are the specific algorithms or techniques used within ASALT?
  • How does ASALT perform in competitive and mixed objective settings?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide detailed information about the specific algorithms or techniques used within ASALT. It only mentions that it uses observation-level and state-level adapters to create shared embeddings.

Raw Abstract / Excerpt

Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives. Prior work has investigated transfer learning between source and target domains in MARL; however, the majority of existing approaches impose the constraint that the dimensionalities of the observation space and the global state space must be identical across domains. In this paper, we introduce a method that explicitly accommodates mismatched state-space dimensionalities between source and target domains. The proposed approach, ASALT, incorporates both observation-level and state-level adapters that map the target-domain observations and global states into a shared embedding space, thereby enabling more effective transfer of knowledge across both actors and critics. These adapters can generate embeddings that support efficient strategy transfer across heterogeneous domains. Experimental results on multiple configurations in standard benchmark environments demonstrate that ASALT surpasses existing baselines in terms of sample efficiency and global return in cooperative settings, but its effectiveness depends on the degree of mismatch between source and target domains. Furthermore, our findings indicate that ASALT mitigates negative transfer, which frequently constitutes a major obstacle when transferring policies between domains with differing observation and action spaces.