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 7651–7675 of 15113 papers

TitleStatusHype
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement LearningCode0
Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles—0
Symmetry-aware Neural Architecture for Embodied Visual Navigation—0
Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach—0
Unsupervised Compressive Text Summarisation with Reinforcement Learning—0
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)Code0
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents—0
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning—0
Deep Reinforcement Learning-based Authentic Dialogue Generation To Protect Youth From Cybergrooming—0
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation—0
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation—0
Unsupervised Reinforcement Learning in Multiple EnvironmentsCode0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
Feature-Attending Recurrent Modules for Generalization in Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning—0
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement LearningCode0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
Quantifying Multimodality in World Models—0
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning—0
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Benchmark Results

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