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 8951–8975 of 15113 papers

TitleStatusHype
Empirical Evaluation of Supervision Signals for Style Transfer Models—0
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Affordance-based Reinforcement Learning for Urban Driving—0
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks—0
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision—0
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments—0
Reinforcement learning based recommender systems: A survey—0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning—0
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time SystemsCode0
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement LearningCode0
Memory-Augmented Reinforcement Learning for Image-Goal NavigationCode1
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service—0
Linear Representation Meta-Reinforcement Learning for Instant Adaptation—0
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning—0
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement LearningCode1
First-Order Problem Solving through Neural MCTS based Reinforcement Learning—0
Action Priors for Large Action Spaces in RoboticsCode0
Independent Policy Gradient Methods for Competitive Reinforcement Learning—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment—0
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs—0
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Benchmark Results

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