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 4171–4180 of 15113 papers

TitleStatusHype
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization—0
Generalization Through the Lens of Learning Dynamics—0
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks—0
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning—0
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model CheckingCode1
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning—0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
Reinforcement Learning for Predicting Traffic Accidents—0
Show:102550
← PrevPage 418 of 1512Next →

Benchmark Results

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