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 5876–5900 of 15113 papers

TitleStatusHype
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Self-supervised Reinforcement Learning with Independently Controllable Subgoals—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation—0
Self-Supervised Structured Representations for Deep Reinforcement Learning—0
Self-timed Reinforcement Learning using Tsetlin Machine—0
Self Training Autonomous Driving Agent—0
A Self-Tuning Actor-Critic Algorithm—0
Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization—0
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks—0
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints—0
Semantic Exploration from Language Abstractions and Pretrained Representations—0
Semantic Guidance of Dialogue Generation with Reinforcement Learning—0
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
Semi-analytical Industrial Cooling System Model for Reinforcement Learning—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Semi-pessimistic Reinforcement Learning—0
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers—0
Semi-Supervised Off Policy Reinforcement Learning—0
Semi-Supervised QA with Generative Domain-Adaptive Nets—0
Semi-supervised reward learning for offline reinforcement learning—0
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets—0
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments—0
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Benchmark Results

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