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 10101–10125 of 15113 papers

TitleStatusHype
Ergodic Annealing—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Queueing Network Controls via Deep Reinforcement LearningCode0
Chance Constrained Policy Optimization for Process Control and Optimization—0
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to RewardsCode1
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents—0
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments—0
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning—0
Munchausen Reinforcement LearningCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective—0
Greedy Bandits with Sampled Context—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation—0
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events—0
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning—0
Fast active learning for pure exploration in reinforcement learning—0
FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation—0
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Benchmark Results

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