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

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Showing 4476–4500 of 15113 papers

TitleStatusHype
Mean Actor CriticCode0
Scaling Laws for a Multi-Agent Reinforcement Learning ModelCode0
Efficient Meta Subspace OptimizationCode0
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching TasksCode0
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless NetworksCode0
Variational Delayed Policy OptimizationCode0
Variational Generative Stochastic Networks with Collaborative ShapingCode0
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal ControlCode0
Variational Information Maximisation for Intrinsically Motivated Reinforcement LearningCode0
Variational Intrinsic ControlCode0
Budgeted Reinforcement Learning in Continuous State SpaceCode0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Variational Quantum Circuits for Deep Reinforcement LearningCode0
Policy Poisoning in Batch Reinforcement Learning and ControlCode0
Neural Architecture Search with Reinforcement LearningCode0
Scheduled Policy Optimization for Natural Language Communication with Intelligent AgentsCode0
Variational Recurrent Models for Solving Partially Observable Control TasksCode0
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous IntegrationCode0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
Model-Based Reinforcement Learning for AtariCode0
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human FeedbackCode0
On the calibration of compartmental epidemiological modelsCode0
Verifiable and Compositional Reinforcement Learning SystemsCode0
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action EffectsCode0
Verifying Controllers Against Adversarial Examples with Bayesian OptimizationCode0
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Benchmark Results

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