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 3726–3750 of 15113 papers

TitleStatusHype
Clipped-Objective Policy Gradients for Pessimistic Policy OptimizationCode0
Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect InformationCode0
Deep reinforcement learning for smart calibration of radio telescopesCode0
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in CopenhagenCode0
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social NetworksCode0
Client Selection for Federated Policy Optimization with Environment HeterogeneityCode0
Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning ProgramsCode0
GAN Q-learningCode0
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement LearningCode0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
Functional Acceleration for Policy Mirror DescentCode0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the CloudCode0
Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille KeyboardCode0
Autoregressive Policies for Continuous Control Deep Reinforcement LearningCode0
AutoRL Hyperparameter LandscapesCode0
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov ModelCode0
Hierarchical Potential-based Reward Shaping from Task SpecificationsCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Classification with Costly Features using Deep Reinforcement LearningCode0
Classification with Costly Features as a Sequential Decision-Making ProblemCode0
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) ZeroCode0
Reinforcement Learning Generalization with Surprise MinimizationCode0
Deep Reinforcement Learning for Traffic Light Control in Vehicular NetworksCode0
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?Code0
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Benchmark Results

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