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Explainable reinforcement learning: A survey and comparative review
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …
learning that has attracted considerable attention in recent years. The goal of XRL is to …
Inductive biases for deep learning of higher-level cognition
A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
Principle-driven self-alignment of language models from scratch with minimal human supervision
Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning
(SFT) with human annotations and reinforcement learning from human feedback (RLHF) to …
(SFT) with human annotations and reinforcement learning from human feedback (RLHF) to …
Do embodied agents dream of pixelated sheep: Embodied decision making using language guided world modelling
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of
the world. However, if initialized with knowledge of high-level subgoals and transitions …
the world. However, if initialized with knowledge of high-level subgoals and transitions …
Interpretable reward redistribution in reinforcement learning: A causal approach
A major challenge in reinforcement learning is to determine which state-action pairs are
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …
SALMON: Self-alignment with instructable reward models
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement
Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM …
Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM …
A dataset perspective on offline reinforcement learning
Abstract The application of Reinforcement Learning (RL) in real world environments can be
expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is …
expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is …