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 4211–4220 of 15113 papers

TitleStatusHype
Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic—0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks—0
Robust Reinforcement Learning for Risk-Sensitive Linear Quadratic Gaussian Control—0
L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement LearningCode0
Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation—0
Physics-Informed Model-Based Reinforcement LearningCode1
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks—0
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed DatasetsCode0
A Machine with Short-Term, Episodic, and Semantic Memory SystemsCode0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
Show:102550
← PrevPage 422 of 1512Next →

Benchmark Results

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