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 4701–4725 of 15113 papers

TitleStatusHype
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence—0
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration—0
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments—0
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
A Spiking Binary Neuron -- Detector of Causal Links—0
Reward Engineering for Generating Semi-structured ExplanationCode0
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation—0
Physically Plausible Full-Body Hand-Object Interaction Synthesis—0
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
Efficient Reinforcement Learning for Jumping MonopodsCode0
Investigating the Impact of Action Representations in Policy Gradient Algorithms—0
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning—0
Safe Reinforcement Learning with Dual Robustness—0
Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures—0
Risk-Aware Reinforcement Learning through Optimal Transport Theory—0
Representation Learning in Low-rank Slate-based Recommender Systems—0
Signal Temporal Logic Neural Predictive Control—0
Verifiable Reinforcement Learning Systems via Compositionality—0
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective—0
Compositional Learning of Visually-Grounded Concepts Using ReinforcementCode0
Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control—0
A State Representation for Diminishing Rewards—0
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Benchmark Results

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