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 2641–2650 of 15113 papers

TitleStatusHype
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Assume-Guarantee Reinforcement Learning—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
Towards Automatic Data Augmentation for Disordered Speech Recognition—0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
World Models via Policy-Guided Trajectory DiffusionCode1
Show:102550
← PrevPage 265 of 1512Next →

Benchmark Results

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