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

Showing 28012825 of 15113 papers

TitleStatusHype
A State-Distribution Matching Approach to Non-Episodic Reinforcement LearningCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active MeasuringCode0
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning PoliciesCode0
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language ModelsCode0
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog SystemsCode0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Assistive Teaching of Motor Control Tasks to HumansCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Improving reinforcement learning algorithms: towards optimal learning rate policiesCode0
Improving Reinforcement Learning Based Image Captioning with Natural Language PriorCode0
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot NavigationCode0
Improving the Performance of Backward Chained Behavior Trees that use Reinforcement LearningCode0
Improving Policy Learning via Language Dynamics DistillationCode0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Improving Policy Optimization with Generalist-Specialist LearningCode0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and ScaleCode0
Depth Self-Optimized Learning Toward Data ScienceCode0
Improving Image Captioning with Conditional Generative Adversarial NetsCode0
Improving Information Extraction by Acquiring External Evidence with Reinforcement LearningCode0
Improving Post-Processing of Audio Event Detectors Using Reinforcement LearningCode0
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
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Benchmark Results

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