arXiv:2503.03660v4 Announce Type: replace Abstract: We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state-action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating multi-step returns -- without importance sampling (IS). The resulting sequence-aware value estimates capture the critical temporal
The paper builds on recent advancements in transformer architectures and reinforcement learning, integrating them into a more robust critic for complex AI tasks.
This development could significantly improve the sample efficiency and performance of AI agents, accelerating their ability to learn and operate in real-world scenarios requiring complex sequential decision-making.
AI agents may become more capable of understanding and acting within longer temporal contexts, moving beyond isolated state-action evaluations to more sophisticated, trajectory-aware planning.
- · AI agents developers
- · Robotics
- · Generative AI
- · Reinforcement learning researchers
- · Traditional reinforcement learning methods
- · AI applications requiring high sample efficiency
Improved performance and broader applicability of reinforcement learning in complex, real-world tasks.
Faster development and deployment of more sophisticated AI agents in various industries, from manufacturing to logistics.
Enhanced automation capabilities across many sectors, potentially leading to significant shifts in labor markets and operational efficiencies.
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Read at arXiv cs.LG