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 4201–4225 of 15113 papers

TitleStatusHype
Misspecification in Inverse Reinforcement Learning—0
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User EngagementCode1
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
Active Classification of Moving Targets with Learned Control Policies—0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic—0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks—0
Robust Reinforcement Learning for Risk-Sensitive Linear Quadratic Gaussian Control—0
L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement LearningCode0
Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation—0
Physics-Informed Model-Based Reinforcement LearningCode1
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks—0
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed DatasetsCode0
A Machine with Short-Term, Episodic, and Semantic Memory SystemsCode0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations—0
Automata Learning meets ShieldingCode0
Online Shielding for Reinforcement Learning—0
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance—0
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement LearningCode1
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Benchmark Results

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