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 10201–10225 of 15113 papers

TitleStatusHype
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction—0
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning—0
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer—0
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Investigation of Sentiment Controllable Chatbot—0
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Simulating multi-exit evacuation using deep reinforcement learning—0
Long-Term Planning with Deep Reinforcement Learning on Autonomous DronesCode1
Vizarel: A System to Help Better Understand RL Agents—0
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Representations for Stable Off-Policy Reinforcement Learning—0
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing—0
Fast reinforcement learning with generalized policy updates—0
Learning Retrospective Knowledge with Reverse Reinforcement Learning—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
Attention or memory? Neurointerpretable agents in space and time—0
EVO-RL: Evolutionary-Driven Reinforcement Learning—0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization—0
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic ControlCode1
On the Reliability and Generalizability of Brain-inspired Reinforcement Learning AlgorithmsCode0
Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning—0
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
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Benchmark Results

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