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 1426–1450 of 15113 papers

TitleStatusHype
ARLO: A Framework for Automated Reinforcement LearningCode1
Learning Synthetic Environments and Reward Networks for Reinforcement LearningCode1
Learning Synthetic Environments for Reinforcement Learning with Evolution StrategiesCode1
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Learning to Act without ActionsCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
Deep Deterministic Portfolio OptimizationCode1
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink PaintingCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
DeepMind Lab2DCode1
BabyAI 1.1Code1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
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Benchmark Results

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