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 6076–6100 of 15113 papers

TitleStatusHype
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance—0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment—0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations—0
Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience—0
Safe Reinforcement Learning for Legged Locomotion—0
Target Network and Truncation Overcome The Deadly Triad in Q-Learning—0
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions—0
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning—0
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction—0
Deep Q-network using reservoir computing with multi-layered readout—0
On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical EfficiencyCode0
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches—0
Testing Stationarity and Change Point Detection in Reinforcement LearningCode1
Quantum Reinforcement Learning via Policy Iteration—0
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning—0
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement LearningCode0
Reliable validation of Reinforcement Learning Benchmarks—0
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power SystemsCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
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Benchmark Results

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