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 4081–4090 of 15113 papers

TitleStatusHype
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
SliceIt! -- A Dual Simulator Framework for Learning Robot Food SlicingCode0
Reinforcement Learning in Categorical Cybernetics—0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Show:102550
← PrevPage 409 of 1512Next →

Benchmark Results

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