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

Showing 1265112700 of 15113 papers

TitleStatusHype
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning0
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape CompletionCode0
Self Training Autonomous Driving Agent0
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban EnvironmentsCode0
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning0
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks0
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning FrameworkCode0
Cognitive Radar Using Reinforcement Learning in Automotive Applications0
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning0
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning0
Evolving Neural Networks in Reinforcement Learning by means of UMDAc0
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning0
Stochastic Lipschitz Q-Learning0
Neural Logic Reinforcement LearningCode0
Baconian: A Unified Open-source Framework for Model-Based Reinforcement LearningCode0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsCode0
Generative Exploration and Exploitation0
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team CompetitionCode0
Compression and Localization in Reinforcement Learning for ATARI Games0
Model-free Deep Reinforcement Learning for Urban Autonomous DrivingCode1
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning0
Emergence of Compositional Language with Deep Generational TransmissionCode0
When is a Prediction Knowledge?0
Making Meaning: Semiotics Within Predictive Knowledge Architectures0
Decoding Molecular Graph Embeddings with Reinforcement Learning0
Improving Interactive Reinforcement Agent Planning with Human Demonstration0
Bayesian policy selection using active inference0
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts0
A Survey on Traffic Signal Control Methods0
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation0
Posterior-regularized REINFORCE for Instance Selection in Distant SupervisionCode0
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
Reinforcement Learning for Nested Polar Code Construction0
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks0
End-to-End Robotic Reinforcement Learning without Reward EngineeringCode0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning0
Improving interactive reinforcement learning: What makes a good teacher?0
Disentangling Options with Hellinger Distance Regularizer0
Curious iLQR: Resolving Uncertainty in Model-based RL0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
Multi-Objective Autonomous Braking System using Naturalistic Dataset0
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving0
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation0
A Short Survey On Memory Based Reinforcement Learning0
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from ObservationsCode0
Effective Scheduling Function Design in SDN through Deep Reinforcement Learning0
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari EnvironmentsCode0
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Benchmark Results

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