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 7476–7500 of 15113 papers

TitleStatusHype
Few-shot Quality-Diversity OptimizationCode0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
Learning to Navigate Intersections with Unsupervised Driver Trait InferenceCode1
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model CheckingCode0
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in RoboticsCode1
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods—0
Learning-to-defer for sequential medical decision-making under uncertainty—0
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
Reinforcement Learning for Load-balanced Parallel Particle Tracing—0
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
Direct Random Search for Fine Tuning of Deep Reinforcement Learning PoliciesCode0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing—0
Concave Utility Reinforcement Learning with Zero-Constraint Violations—0
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles—0
Learning Selective Communication for Multi-Agent Path FindingCode1
A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem—0
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation—0
Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning—0
Physics-based Deep LearningCode2
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning—0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems—0
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Benchmark Results

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