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 5126–5150 of 15113 papers

TitleStatusHype
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
Offline Reinforcement Learning at Multiple Frequencies—0
Semi-analytical Industrial Cooling System Model for Reinforcement Learning—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—0
Cooperative Actor-Critic via TD Error Aggregation—0
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks—0
Live in the Moment: Learning Dynamics Model Adapted to Evolving PolicyCode0
Online Reinforcement Learning for Periodic MDP—0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations—0
Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement LearningCode0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Lifelong Machine Learning of Functionally Compositional StructuresCode1
Learning Soccer Juggling Skills with Layer-wise Mixture-of-ExpertsCode1
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System—0
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous DrivingCode1
Halftoning with Multi-Agent Deep Reinforcement Learning—0
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning—0
Hierarchical Kickstarting for Skill Transfer in Reinforcement LearningCode1
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution—0
Robust Knowledge Adaptation for Dynamic Graph Neural NetworksCode1
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Strategising template-guided needle placement for MR-targeted prostate biopsy—0
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Benchmark Results

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