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 12151–12200 of 15113 papers

TitleStatusHype
PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning—0
Inverse Reinforcement Learning with Multiple Ranked Experts—0
Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learningCode0
Multi-Agent Adversarial Inverse Reinforcement LearningCode0
Wasserstein Robust Reinforcement Learning—0
Model-Free Unsupervised Learning for Optimization Problems with Constraints—0
Reward Learning for Efficient Reinforcement Learning in Extractive Document SummarisationCode0
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters—0
MineRL: A Large-Scale Dataset of Minecraft DemonstrationsCode0
Hindsight Trust Region Policy OptimizationCode0
Goal-Driven Sequential Data Abstraction—0
Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task AbstractionsCode0
Taxable Stock Trading with Deep Reinforcement Learning—0
Towards Model-based Reinforcement Learning for Industry-near EnvironmentsCode0
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman—0
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Environment Probing Interaction PoliciesCode0
Large scale continuous-time mean-variance portfolio allocation via reinforcement learning—0
Action Guidance with MCTS for Deep Reinforcement Learning—0
Interactive Lungs Auscultation with Reinforcement Learning Agent—0
Google Research Football: A Novel Reinforcement Learning EnvironmentCode0
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving—0
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks—0
Fairness in Reinforcement Learning—0
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts—0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
Metalearned Neural MemoryCode0
Structured Fusion Networks for DialogCode0
Modeling question asking using neural program generationCode0
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language InferenceCode0
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges—0
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation—0
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning—0
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications—0
Characterizing Attacks on Deep Reinforcement LearningCode0
Techniques for Automated Machine Learning—0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
Accelerating Reinforcement Learning through GPU Atari EmulationCode0
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
Self-Attentional Credit Assignment for Transfer in Reinforcement LearningCode0
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery—0
Convolutional Reservoir Computing for World ModelsCode0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning—0
Photonic architecture for reinforcement learning—0
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Benchmark Results

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