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 3576–3600 of 15113 papers

TitleStatusHype
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Recommending the optimal policy by learning to act from temporal data—0
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning—0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
Smoothed Q-learning—0
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems—0
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning—0
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model—0
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Fast Rates for Maximum Entropy ExplorationCode0
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active MeasuringCode0
Loss of Plasticity in Continual Deep Reinforcement Learning—0
Actor-Critic learning for mean-field control in continuous time—0
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication—0
Path Planning using Reinforcement Learning: A Policy Iteration Approach—0
Kernel Density Bayesian Inverse Reinforcement LearningCode0
Deploying Offline Reinforcement Learning with Human Feedback—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Transformer-based World Models Are Happy With 100k InteractionsCode1
Synthetic Experience ReplayCode1
Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi—0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
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Benchmark Results

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