SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 11426–11450 of 15113 papers

TitleStatusHype
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning—0
Human-centered collaborative robots with deep reinforcement learning—0
Human-centered mechanism design with Democratic AI—0
Human-centric Dialog Training via Offline Reinforcement Learning—0
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment—0
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning—0
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems—0
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning—0
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning—0
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences—0
Human-Object Interaction from Human-Level Instructions—0
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning—0
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives—0
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified