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 11601–11625 of 15113 papers

TitleStatusHype
Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT—0
Learning Sparse Representations Incrementally in Deep Reinforcement Learning—0
ChainerRL: A Deep Reinforcement Learning Library—0
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
Learning Latent State Spaces for Planning through Reward Prediction—0
Efficient Object Detection in Large Images using Deep Reinforcement LearningCode0
Unsupervised Curricula for Visual Meta-Reinforcement Learning—0
Transformer Based Reinforcement Learning For Games—0
Optimism in Reinforcement Learning with Generalized Linear Function Approximation—0
Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning—0
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learningCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions—0
No-Regret Exploration in Goal-Oriented Reinforcement Learning—0
Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop—0
Observational Overfitting in Reinforcement Learning—0
A pedestrian path-planning model in accordance with obstacle's danger with reinforcement learning—0
How Does an Approximate Model Help in Reinforcement Learning?—0
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles—0
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization—0
Iterative Policy-Space Expansion in Reinforcement Learning—0
Reinforcement Learning with Non-Markovian Rewards—0
Hindsight Credit AssignmentCode0
Inter-Level Cooperation in Hierarchical Reinforcement LearningCode0
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Benchmark Results

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