The article discusses ACE (Off-Policy Actor-Critic with Causality-Aware Entropy Regularization), a novel reinforcement learning (RL) algorithm. Traditional RL algorithms often overlook the varying significance of distinct primitive behaviors during the…
Continue readingThis article discusses the application of Reinforcement Learning from Human Feedback (RLHF) in large language models (LLMs). It critically examines the use of Proximal Policy Optimization (PPO), which though popular,…
Continue readingPreference learning aims to align the generations of large language models (LLMs) with human preferences. Most previous work focuses on in-distribution preference learning, but this research addresses out-of-distribution (OOD) preference…
Continue readingThe article presents a reinforcement learning approach to solve the Stochastic Vehicle Routing Problem with Time Windows (SVRP). The goal is to optimize goods delivery by reducing travel costs. The…
Continue readingThe paper ‘Jack of All Trades, Master of Some’ introduces a transformer-based model, JAT, optimized for handling multi-modal data types and sequential decision-making tasks. The model demonstrates its versatility by…
Continue readingThis academic paper presents ACTER (Actionable CounTerfactual Sequences for Explaining Reinforcement Learning Outcomes), a new algorithm for generating counterfactual sequences to help understand and prevent failures in reinforcement learning. The…
Continue readingThe paper integrates the theory of reinforcement learning with work on cognition and neuroscience. It pays special attention to the successor representation (SR) and its generalizations, which have been widely…
Continue readingThis study presents three pathways to implementing models and policies in neurosymbolic artificial intelligence (AI), which combines the interpretability and explicit reasoning of symbolic approaches with data-driven neural approaches. The…
Continue readingThe study presents a framework for hybrid teams comprising of humans and autonomous systems, or agents. The authors propose an AI manager, equipped with Reinforcement Learning, to monitor the team…
Continue readingThis research paper presents R3, a novel method for training large language models (LLMs) in complex reasoning tasks. R3 uses reverse curriculum reinforcement learning (RL), which provides the benefits of…
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