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 2601–2610 of 15113 papers

TitleStatusHype
General Method for Solving Four Types of SAT Problems—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
Learning Online Policies for Person Tracking in Multi-View Environments—0
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement LearningCode1
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
Agent based modelling for continuously varying supply chains—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
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Benchmark Results

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