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 8176–8200 of 15113 papers

TitleStatusHype
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems—0
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Stability Constrained Reinforcement Learning for Real-Time Voltage Control—0
Neural Network Verification in Control—0
Surveillance Evasion Through Bayesian Reinforcement LearningCode0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Reinforcement Learning with Information-Theoretic Actuation—0
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators—0
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
An Attempt to Model Human Trust with Reinforcement Learning—0
Fully Decentralized Model-based Policy Optimization with Networked Agents—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
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Benchmark Results

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