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 7701–7725 of 15113 papers

TitleStatusHype
Tianshou: a Highly Modularized Deep Reinforcement Learning LibraryCode3
Non-Markovian Reinforcement Learning using Fractional Dynamics—0
Lyapunov-based uncertainty-aware safe reinforcement learning—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation—0
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming—0
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward AlgorithmCode1
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts—0
Autonomous Reinforcement Learning via Subgoal Curricula—0
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent—0
Playtesting: What is Beyond Personas—0
Reinforced Imitation Learning by Free Energy Principle—0
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Learning Quadruped Locomotion Policies using Logical Rules—0
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
A reinforcement learning approach to resource allocation in genomic selection—0
Accelerating Quadratic Optimization with Reinforcement LearningCode1
A Deep Reinforcement Learning Approach for Fair Traffic Signal Control—0
Demonstration-Guided Reinforcement Learning with Learned SkillsCode1
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated EnvironmentsCode1
Show:102550
← PrevPage 309 of 605Next →

Benchmark Results

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