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 5701–5725 of 15113 papers

TitleStatusHype
Few-Shot Preference Learning for Human-in-the-Loop RL—0
Adaptive Risk-Aware Bidding with Budget Constraint in Display AdvertisingCode0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
Active Classification of Moving Targets with Learned Control Policies—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement LearningCode0
Robust Reinforcement Learning for Risk-Sensitive Linear Quadratic Gaussian Control—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
A Machine with Short-Term, Episodic, and Semantic Memory SystemsCode0
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
Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation—0
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations—0
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks—0
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed DatasetsCode0
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance—0
Online Shielding for Reinforcement Learning—0
Automata Learning meets ShieldingCode0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics—0
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward—0
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
Show:102550
← PrevPage 229 of 605Next →

Benchmark Results

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