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 10901–10950 of 15113 papers

TitleStatusHype
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D EnvironmentsCode1
Reinforcement Learning in Economics and Finance—0
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation—0
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics—0
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach—0
Deep Reinforcement Learning with Robust and Smooth Policy—0
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
Deep Sets for Generalization in RL—0
Deep Constrained Q-learning—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Safe Reinforcement Learning of Control-Affine Systems with Vertex NetworksCode0
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks—0
Towards Cognitive Routing based on Deep Reinforcement Learning—0
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Adjust Planning Strategies to Accommodate Reinforcement Learning AgentsCode0
Exchangeable Input Representations for Reinforcement Learning—0
Learning to Fly via Deep Model-Based Reinforcement LearningCode0
Robust Deep Reinforcement Learning against Adversarial Perturbations on State ObservationsCode1
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles—0
Viewport-Aware Deep Reinforcement Learning Approach for 360^o Video Caching—0
Placement Optimization with Deep Reinforcement Learning—0
Social Navigation with Human Empowerment driven Deep Reinforcement LearningCode1
Neuroevolution of Self-Interpretable AgentsCode2
Giving Up Control: Neurons as Reinforcement Learning AgentsCode1
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation AssuranceCode0
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement LearningCode1
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems—0
Self-Supervised Discovering of Interpretable Features for Reinforcement LearningCode1
PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy NetworkCode1
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks—0
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Reinforcement Learning for Electricity Network Operation—0
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised LearningCode0
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous—0
Deep Deterministic Portfolio OptimizationCode1
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation—0
Taylor Expansion Policy Optimization—0
A General Framework for Learning Mean-Field Games—0
Analyzing Visual Representations in Embodied Navigation Tasks—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Sample Efficient Reinforcement Learning through Learning from Demonstrations in MinecraftCode1
The Chef's Hat Simulation Environment for Reinforcement-Learning-Based AgentsCode0
Option Discovery in the Absence of Rewards with Manifold AnalysisCode0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach—0
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning—0
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks—0
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Benchmark Results

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