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 7851–7875 of 15113 papers

TitleStatusHype
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement LearningCode1
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Blending Task Success and User Satisfaction: Analysis of Learned Dialogue Behaviour with Multiple Rewards—0
Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning—0
Goal-Conditioned Reinforcement Learning with Imagined Subgoals—0
Distilling Reinforcement Learning Tricks for Video GamesCode1
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data AugmentationCode1
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-EnsembleCode1
MHER: Model-based Hindsight Experience Replay—0
Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning—0
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic RewardsCode1
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning—0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting—0
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents—0
Koopman Spectrum Nonlinear Regulators and Efficient Online LearningCode0
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Understanding Adversarial Attacks on Observations in Deep Reinforcement LearningCode0
Reinforcement Learning based Disease Progression Model for Alzheimer's Disease—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Structure-aware reinforcement learning for node-overload protection in mobile edge computing—0
Deep Multiagent Reinforcement Learning: Challenges and Directions—0
Analysis and Control of a Planar QuadrotorCode0
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Benchmark Results

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