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 8526–8550 of 15113 papers

TitleStatusHype
Human-Inspired Multi-Agent Navigation using Knowledge DistillationCode1
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning—0
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services—0
Maximum Entropy Reinforcement Learning with Mixture Policies—0
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety CagesCode0
Near Optimal Policy Optimization via REPS—0
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
A Practical Guide to Multi-Objective Reinforcement Learning and PlanningCode0
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design—0
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
Learning to Shape Rewards using a Game of Two Partners—0
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving—0
Lyapunov Barrier Policy OptimizationCode1
Transfer Learning for Automated Test Case Prioritization Using XCSFCode0
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning—0
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Autonomous Drone Racing with Deep Reinforcement Learning—0
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning—0
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning—0
Reinforcement Learning with Algorithms from Probabilistic Structure EstimationCode0
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics ModelCode1
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction—0
Offline Reinforcement Learning with Fisher Divergence Critic Regularization—0
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Benchmark Results

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