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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10626–10650 of 15113 papers

TitleStatusHype
Data-driven Dynamic Multi-objective Optimal Control: An Aspiration-satisfying Reinforcement Learning Approach—0
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Probabilistic Guarantees for Safe Deep Reinforcement Learning—0
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning—0
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics—0
Explainable Reinforcement Learning: A Survey—0
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms—0
MOReL : Model-Based Offline Reinforcement LearningCode1
Training spiking neural networks using reinforcement learningCode1
Planning to Explore via Self-Supervised World ModelsCode1
Smooth Exploration for Robotic Reinforcement LearningCode2
Reinforcement Learning Based on Real-Time Iteration NMPC—0
Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement LearningCode1
TOMA: Topological Map Abstraction for Reinforcement Learning—0
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Delay-Aware Model-Based Reinforcement Learning for Continuous ControlCode1
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support—0
Deep Reinforcement Learning for Organ Localization in CT—0
Maximizing Information Gain in Partially Observable Environments via Prediction Reward—0
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLCode1
A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar—0
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Benchmark Results

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