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

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Showing 10551–10575 of 15113 papers

TitleStatusHype
Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel—0
EasyRL: A Simple and Extensible Reinforcement Learning Framework—0
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
A Relearning Approach to Reinforcement Learning for Control of Smart Buildings—0
Learning Transition Models with Time-delayed Causal Relations—0
Learning to Play Two-Player Perfect-Information Games without Knowledge—0
Learning Agile Locomotion via Adversarial Training—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power—0
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
Proximal Deterministic Policy Gradient—0
Curriculum Learning with a Progression Function—0
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs—0
Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things—0
Ergodic Annealing—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning—0
Queueing Network Controls via Deep Reinforcement LearningCode0
Deep Reinforcement Learning using Cyclical Learning Rates—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Chance Constrained Policy Optimization for Process Control and Optimization—0
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents—0
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments—0
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Benchmark Results

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