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 3901–3925 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Optimism and Delays in Episodic Reinforcement Learning—0
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning—0
Delayed Reinforcement Learning by Imitation—0
Delays in Reinforcement Learning—0
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Delving into adversarial attacks on deep policies—0
Delving into Macro Placement with Reinforcement Learning—0
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks—0
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning—0
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation—0
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction—0
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
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Benchmark Results

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