ASALT: Adaptive State Alignment for Lateral Transfer in MARL

ASALT is a transfer method for multi-agent reinforcement learning that adapts when source and target domains have different observation or global state dimensionalities. Submitted to arXiv as arXiv:2606.24601 on 23 Jun 2026 and accepted at RLC 2026, the paper is authored by Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar and Kaushik Dey.

What is ASALT and how does it work?

ASALT uses two kinds of adapters—observation-level and state-level—that map target-domain observations and global states into a shared embedding space, enabling knowledge transfer across both actors and critics. The adapters generate embeddings that support strategy transfer across heterogeneous domains, explicitly accommodating mismatched state-space dimensionalities that prior work typically required to be identical.

The method positions the mapping step inside both the actor and critic pathways so that policies and value functions can operate on a common representation even when raw observation and global state vectors differ in size between source and target. The paper frames this as a lateral transfer problem in multi-agent reinforcement learning where heterogeneity in sensors or global information is the primary barrier to reuse.

How does ASALT perform compared to baselines?

ASALT outperforms existing baselines in cooperative benchmark settings on two key axes: sample efficiency and final global return, though its effectiveness varies with the degree of mismatch between source and target domains. Experimental results reported in the paper show consistent gains in these cooperative settings while noting the methods sensitivity to how different the domains are.

The authors highlight that ASALT not only improves learning speed but also addresses a common transfer failure mode: negative transfer. They report that ASALT "mitigates negative transfer," which the paper identifies as a frequent obstacle when transferring policies across domains with differing observation and action spaces. The experiments span multiple configurations in standard benchmark environments, but the abstract does not publish numeric improvements or specific environment names.

Why does it matter?

ASALT relaxes a restrictive assumption in much prior MARL transfer work: that observation and global state dimensionalities must match across domains. By building adapters that produce a shared embedding, ASALT enables actors and critics to reuse strategies across heterogeneous setups, expanding the set of feasible transfer pairs. This matters for research and applications where agents face different sensors, partial observability, or varied state encodings, because it reduces the engineering required to align domains before transfer.

In cooperative multi-agent tasks, improving sample efficiency and global return directly affects how quickly teams of agents can learn coordinated behaviors. The papers acceptance at RLC 2026 signals peer interest in methods that address structural heterogeneity in MARL transfer.

What to watch

Look for the conference presentation and the full paper at RLC 2026 for detailed experimental numbers, environment names, and ablations that quantify how ASALTs effectiveness scales with the degree of domain mismatch. The authors have made the submission available on arXiv as arXiv:2606.24601 (submitted 23 Jun 2026), which will let readers inspect architectures and experiments directly.