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 2241–2250 of 15113 papers

TitleStatusHype
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Molecular Generative Adversarial Network with Multi-Property Optimization—0
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations—0
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
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Benchmark Results

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