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

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Showing 991–1000 of 15113 papers

TitleStatusHype
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction ExperimentsCode1
O-MAPL: Offline Multi-agent Preference Learning—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
Test-Time Training Scaling Laws for Chemical Exploration in Drug DesignCode3
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Benchmark Results

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