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 7601–7650 of 15113 papers

TitleStatusHype
Implicitly Regularized RL with Implicit Q-Values—0
Using Cyber Terrain in Reinforcement Learning for Penetration Testing—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Neural-to-Tree Policy Distillation with Policy Improvement Criterion—0
Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting—0
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing—0
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs—0
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement LearningCode1
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov ModelCode0
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid EnvironmentsCode0
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
A general class of surrogate functions for stable and efficient reinforcement learningCode0
Reinforcement Learning Approach to Active Learning for Image Classification—0
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning—0
Gap-Dependent Unsupervised Exploration for Reinforcement LearningCode0
Fairness Through Counterfactual UtilitiesCode0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
Integrating process design and control using reinforcement learning—0
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?—0
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning—0
Truncated Emphatic Temporal Difference Methods for Prediction and Control—0
High Quality Related Search Query Suggestions using Deep Reinforcement Learning—0
Imitation Learning by Reinforcement LearningCode0
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey—0
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
Knowledge accumulating: The general pattern of learning—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Paint Transformer: Feed Forward Neural Painting with Stroke PredictionCode1
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action SpacesCode1
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning—0
VeRLPy: Python Library for Verification of Digital Designs with Reinforcement LearningCode1
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization—0
Meta-Reinforcement Learning in Broad and Non-Parametric EnvironmentsCode0
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning—0
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents—0
Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning—0
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI EconomistCode1
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
What Matters in Learning from Offline Human Demonstrations for Robot ManipulationCode2
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents—0
An Elementary Proof that Q-learning Converges Almost Surely—0
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey—0
Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning—0
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Benchmark Results

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