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Found 5 papers, 4 papers with code
Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents
In contrast, agents tested in dynamic robot environments face limitations due to simplistic environments with only a few objects and interactions.
Action Advising with Advice Imitation in Deep Reinforcement Learning
Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning.
Learning on a Budget via Teacher Imitation
However, due to the realistic concerns, the number of these interactions is limited with a budget; therefore, it is crucial to perform these in the most appropriate moments.
Student-Initiated Action Advising via Advice Novelty
Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL).
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning
Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully.
Multi-agent Reinforcement Learning
reinforcement-learning
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