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 8726–8750 of 15113 papers

TitleStatusHype
Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning—0
Gradient Importance Learning for Incomplete ObservationsCode0
A Review of Explainable Artificial Intelligence in Manufacturing—0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
Control of rough terrain vehicles using deep reinforcement learning—0
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces—0
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement LearningCode0
Examining average and discounted reward optimality criteria in reinforcement learning—0
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement LearningCode0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning—0
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensingCode0
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents—0
Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning—0
MHER: Model-based Hindsight Experience Replay—0
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning—0
Blending Task Success and User Satisfaction: Analysis of Learned Dialogue Behaviour with Multiple Rewards—0
Goal-Conditioned Reinforcement Learning with Imagined Subgoals—0
Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning—0
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
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Benchmark Results

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