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 901–950 of 15113 papers

TitleStatusHype
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching AgentCode1
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Continuous control with deep reinforcement learningCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Gated Hierarchical Attention for Image CaptioningCode1
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement LearningCode1
Contextualized Rewriting for Text SummarizationCode1
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
Generalization to New Actions in Reinforcement LearningCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Constructions in combinatorics via neural networksCode1
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement LearningCode1
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
A Max-Min Entropy Framework for Reinforcement LearningCode1
Giving Up Control: Neurons as Reinforcement Learning AgentsCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
Accelerating Exploration with Unlabeled Prior DataCode1
A Meta-Reinforcement Learning Algorithm for Causal DiscoveryCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-DemandCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Active Exploration for Inverse Reinforcement LearningCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Deep Active Inference for Partially Observable MDPsCode1
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Benchmark Results

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