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 3771–3780 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning in a Monetary Model—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
Deep Reinforcement Learning in Cryptocurrency Market Making—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning—0
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Average-Reward Learning and Planning with Options—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Show:102550
← PrevPage 378 of 1512Next →

Benchmark Results

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