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 9626–9650 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks—0
Affordance-based Reinforcement Learning for Urban Driving—0
Empirical Evaluation of Supervision Signals for Style Transfer Models—0
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments—0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
Reinforcement learning based recommender systems: A survey—0
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time SystemsCode0
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning—0
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement LearningCode0
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning—0
Linear Representation Meta-Reinforcement Learning for Instant Adaptation—0
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Independent Policy Gradient Methods for Competitive Reinforcement Learning—0
First-Order Problem Solving through Neural MCTS based Reinforcement Learning—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Action Priors for Large Action Spaces in RoboticsCode0
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment—0
Deep Reinforcement Learning with Function Properties in Mean Reversion StrategiesCode0
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs—0
Safe Coupled Deep Q-Learning for Recommendation Systems—0
qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems—0
CoachNet: An Adversarial Sampling Approach for Reinforcement Learning—0
Coding for Distributed Multi-Agent Reinforcement Learning—0
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
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Benchmark Results

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