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 1101–1150 of 15113 papers

TitleStatusHype
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Action Branching Architectures for Deep Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
A Deep Reinforcement Learning Framework for the Financial Portfolio Management ProblemCode1
Contrastive Active InferenceCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Continuous control with deep reinforcement learningCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Contextualized Rewriting for Text SummarizationCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Compile Scene Graphs with Reinforcement LearningCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
An Introduction to Deep Reinforcement LearningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Constructions in combinatorics via neural networksCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
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Benchmark Results

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