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 12801–12850 of 15113 papers

TitleStatusHype
Evolving Neural Networks in Reinforcement Learning by means of UMDAc—0
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning—0
Neural Logic Reinforcement LearningCode0
Stochastic Lipschitz Q-Learning—0
Baconian: A Unified Open-source Framework for Model-Based Reinforcement LearningCode0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsCode0
Generative Exploration and Exploitation—0
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team CompetitionCode0
Compression and Localization in Reinforcement Learning for ATARI Games—0
Emergence of Compositional Language with Deep Generational TransmissionCode0
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning—0
Improving Interactive Reinforcement Agent Planning with Human Demonstration—0
Decoding Molecular Graph Embeddings with Reinforcement Learning—0
Making Meaning: Semiotics Within Predictive Knowledge Architectures—0
When is a Prediction Knowledge?—0
Posterior-regularized REINFORCE for Instance Selection in Distant SupervisionCode0
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation—0
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor FailuresCode0
Rogue-Gym: A New Challenge for Generalization in Reinforcement LearningCode0
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts—0
A Survey on Traffic Signal Control Methods—0
Bayesian policy selection using active inference—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
End-to-End Robotic Reinforcement Learning without Reward EngineeringCode0
Reinforcement Learning for Nested Polar Code Construction—0
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks—0
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving—0
Multi-Objective Autonomous Braking System using Naturalistic Dataset—0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
Improving interactive reinforcement learning: What makes a good teacher?—0
Disentangling Options with Hellinger Distance Regularizer—0
Curious iLQR: Resolving Uncertainty in Model-based RL—0
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation—0
A Short Survey On Memory Based Reinforcement Learning—0
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from ObservationsCode0
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari EnvironmentsCode0
Effective Scheduling Function Design in SDN through Deep Reinforcement Learning—0
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)—0
Knowledge Flow: Improve Upon Your Teachers—0
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey—0
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman—0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning—0
Teaching GANs to Sketch in Vector Format—0
Reinforced Imitation in Heterogeneous Action Space—0
Reinforcement Learning with Attention that Works: A Self-Supervised Approach—0
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Benchmark Results

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