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 9601–9650 of 15113 papers

TitleStatusHype
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers—0
Solving optimal stopping problems with Deep Q-Learning—0
Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
Feature Selection Using Reinforcement Learning—0
BF++: a language for general-purpose program synthesisCode0
Theory of Mind for Deep Reinforcement Learning in HanabiCode0
Prior Preference Learning from Experts:Designing a Reward with Active Inference—0
Model-based Policy Search for Partially Measurable Systems—0
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing—0
Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning—0
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination—0
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning—0
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments—0
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search—0
Regularized Policies are Reward Robust—0
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask—0
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees—0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Deep Reinforcement Learning with Embedded LQR Controllers—0
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning—0
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision—0
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