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 5626–5650 of 15113 papers

TitleStatusHype
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Inverse Reinforcement Learning for Text Summarization—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Near-optimal Policy Identification in Active Reinforcement Learning—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Quantum policy gradient algorithms—0
Risk-Sensitive Reinforcement Learning with Exponential Criteria—0
Neural Coreference Resolution based on Reinforcement Learning—0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off—0
Pre-Trained Image Encoder for Generalizable Visual 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
Latent Variable Representation for Reinforcement Learning—0
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Reinforcement Learning for Agile Active Target Sensing with a UAV—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Residual Policy Learning for Powertrain Control—0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Emergent Behaviors in Multi-Agent Target Acquisition—0
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Benchmark Results

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