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 8601–8625 of 15113 papers

TitleStatusHype
Challenges for Reinforcement Learning in Healthcare—0
A Scavenger Hunt for Service RobotsCode0
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation—0
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning—0
A Learning-Based Computational Impact Time Guidance—0
Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps—0
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning—0
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Adversarial Reinforcement Learning for Procedural Content Generation—0
Behavior From the Void: Unsupervised Active Pre-TrainingCode1
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control—0
Instabilities of Offline RL with Pre-Trained Neural Representation—0
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
A Crash Course on Reinforcement LearningCode1
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments—0
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous RacingCode1
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning—0
Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket—0
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data—0
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning—0
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning—0
Passing Through Narrow Gaps with Deep Reinforcement Learning—0
Show:102550
← PrevPage 345 of 605Next →

Benchmark Results

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