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Found 5 papers, 1 papers with code
XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making
The explanation component includes a why-choose explanation, a why-not-choose explanation, and a set of reason-elements that underlie the LLM's decision.
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem.
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs).
Sequence-level Intrinsic Exploration Model for Partially Observable Domains
First, we propose a new reasoning paradigm to infer the novelty for the partially observable states, which is built upon forward dynamics prediction.
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return.
