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cs.LG2026
Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
Narjes Nourzad, Carlee Joe-Wong
In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitati…
cs.LG2026
MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
Narjes Nourzad, Carlee Joe-Wong
Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can pr…