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 1651–1660 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning from Images with Latent Space ModelsCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing ProblemsCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
High-Throughput Synchronous Deep RLCode1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
Reinforcement Learning for Contact-Rich Tasks: Robotic Peg Insertion StrategiesCode1
Policy Gradient RL Algorithms as Directed Acyclic GraphsCode1
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Benchmark Results

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