aiqbench analysis
This research addresses the challenge of extending counterfactual credit assignment from discrete to continuous action spaces in multi-agent reinforcement learning (MARL). The proposed SAFE framework utilizes a self-evolving default action sampled from experience buffers, eliminating bias in policy gradients and ensuring convergence without requiring additional simulations or environment-specific priors. Experimental results on cooperative vehicular tasks confirm that this approach outperforms existing state-of-the-art models while maintaining theoretical guarantees for local optima convergence.
A Self-Evolving Default Action for Cooperative Tasks with Continuous Action Space
arXiv:2607.18597v1 Announce Type: new Abstract: Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Carlo sampling often introduce bias into policy gradients and fail to guarantee convergence to local optima, as the sampled actions may not have been sufficiently trained. To address these limitations, we propose SAFE, a novel MARL framework that employs a counterfactual baseline conditioned on a self-evolving default action sampled from each agent's experience buffer. This design naturally extends to continuous action spaces without relying on additional simulations, reward models, or environment-specific prior knowledge. The baseline accurately quantifies each agent's contribution, and introduces no bias into the deterministic policy gradient, ensuring convergence to local optima. Extensive experiments on cooperative vehicular tasks demonstrate that SAFE consistently outperforms state-of-the-art models.
Abstract:Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Carlo sampling often introduce bias into policy gradients and fail to guarantee convergence to local optima, as the sampled actions may not have been sufficiently trained. To address these limitations, we propose SAFE, a novel MARL framework that employs a counterfactual baseline conditioned on a self-evolving default action sampled from each agent's experience buffer. This design naturally extends to continuous action spaces without relying on additional simulations, reward models, or environment-specific prior knowledge. The baseline accurately quantifies each agent's contribution, and introduces no bias into the deterministic policy gradient, ensuring convergence to local optima. Extensive experiments on cooperative vehicular tasks demonstrate that SAFE consistently outperforms state-of-the-art models.
Machine Learning (cs.LG); Multiagent Systems (cs.MA)
arXiv:2607.18597 [cs.LG]
(or arXiv:2607.18597v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.18597 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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