Transformer critic boosts reinforcement learning for long-horizon tasks

Researchers have developed a new sequence-conditioned critic for Soft Actor-Critic (SAC) that uses a lightweight Transformer to model trajectory context. This approach integrates N-step returns without importance sampling, allowing it to capture temporal structure for long-horizon and sparse-reward problems. The method demonstrates consistent performance improvements over standard SAC and other baselines on local-motion benchmarks, particularly for long-trajectory control tasks. AI

IMPACT Enhances reinforcement learning capabilities for complex, long-horizon tasks by improving value estimation.

RANK_REASON The cluster contains an academic paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Dong Tian
• Soft Actor-Critic
• Transformer

• paper
• other

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