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 4126–4150 of 15113 papers

TitleStatusHype
Near-optimal Policy Identification in Active Reinforcement Learning—0
Neural Coreference Resolution based on Reinforcement Learning—0
Risk-Sensitive Reinforcement Learning with Exponential Criteria—0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off—0
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning—0
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
Latent Variable Representation for Reinforcement Learning—0
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Reinforcement Learning for Agile Active Target Sensing with a UAV—0
Offline Reinforcement Learning for Visual NavigationCode1
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability—0
Emergent Behaviors in Multi-Agent Target Acquisition—0
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
Residual Policy Learning for Powertrain Control—0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Reinforcement Learning in System Identification—0
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning—0
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Benchmark Results

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