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 1076–1100 of 15113 papers

TitleStatusHype
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Constructions in combinatorics via neural networksCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Continuous control with deep reinforcement learningCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Concise Reasoning via Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
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Benchmark Results

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