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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9276–9300 of 15113 papers

TitleStatusHype
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Model-Agnostic Learning to Meta-Learn—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation—0
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER—0
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
Improving Neural Machine Translation for Sanskrit-English—0
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue—0
Answer-driven Deep Question Generation based on Reinforcement Learning—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
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Benchmark Results

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