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 5051–5075 of 15113 papers

TitleStatusHype
Reinforcement Learning to Rank with Coarse-grained Labels—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model—0
Reinforcement Learning: Tutorial and Survey—0
Reinforcement Learning Under Algorithmic Triage—0
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory—0
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism—0
Reinforcement Learning under Model Mismatch—0
Reinforcement Learning under Partial Observability Guided by Learned Environment Models—0
Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows—0
Reinforcement Learningx2013Based Transient Response Shaping for Microgrids—0
Reinforcement Learning using Augmented Neural Networks—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Reinforcement Learning using Guided Observability—0
Reinforcement Learning using Kernel-Based Stochastic Factorization—0
Reinforcement Learning Using Quantum Boltzmann Machines—0
Reinforcement Learning via AIXI Approximation—0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Reinforcement Learning via Reasoning from Demonstration—0
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines—0
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning—0
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control—0
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation—0
Reinforcement Learning with Almost Sure Constraints—0
Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention—0
Show:102550
← PrevPage 203 of 605Next →

Benchmark Results

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