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 8526–8550 of 15113 papers

TitleStatusHype
Trends in Neural Architecture Search: Towards the Acceleration of Search—0
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning—0
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task—0
Monolithic vs. hybrid controller for multi-objective Sim-to-Real learningCode0
The Ecosystem Path to General AI—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning—0
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic DelaysCode0
Using Cyber Terrain in Reinforcement Learning for Penetration Testing—0
Neural-to-Tree Policy Distillation with Policy Improvement Criterion—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation—0
Heterotic String Model Building with Monad Bundles and Reinforcement Learning—0
Implicitly Regularized RL with Implicit Q-Values—0
Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting—0
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs—0
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—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
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
Reinforcement Learning Approach to Active Learning for Image Classification—0
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning—0
A general class of surrogate functions for stable and efficient reinforcement learningCode0
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Benchmark Results

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