Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

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

Source

arXiv

arxiv.org/abs/2606.24601v1

Read the source

Source

Plain-Language Brief

The paper introduces ASALT, a method for multi-agent reinforcement learning (MARL) that allows for knowledge transfer between source and target domains even if the state space dimensions do not match. ASALT uses adapters to create embeddings that support strategy transfer across heterogeneous domains. Experiments in various benchmark environments show that ASALT outperforms existing baselines in cooperative settings, but effectiveness depends on the degree of mismatch between source and target domains. It also mitigates negative transfer, a common problem when transferring policies between domains with different observation and action spaces.

What the Document Says

  • The paper presents ASALT, a method for multi-agent reinforcement learning (MARL).
  • ASALT accommodates mismatched state-space dimensionalities between source and target domains in MARL.
  • ASALT uses observation-level and state-level adapters to create embeddings that support strategy transfer across heterogeneous domains.
  • Experiments were conducted on multiple configurations in standard benchmark environments.
  • 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, a common problem when transferring policies between domains with different observation and action spaces.

Key Concepts Mentioned

  • multi-agent reinforcement learning
  • transfer learning
  • state space
  • adapters
  • embeddings
  • negative transfer

Claims Made by the Document

  • ASALT outperforms existing baselines in cooperative settings.
  • The effectiveness of ASALT depends on the degree of mismatch between source and target domains.
  • ASALT mitigates negative transfer.

Evidence Used

  • Experimental results on multiple configurations in standard benchmark environments.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.