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 9526–9550 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Online Observer-Based Inverse Reinforcement Learning—0
Generalization to New Actions in Reinforcement LearningCode1
Self-Driving Network and Service Coordination Using Deep Reinforcement LearningCode1
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Exact Asymptotics for Linear Quadratic Adaptive ControlCode0
Incorporating Rivalry in Reinforcement Learning for a Competitive Game—0
Depth Self-Optimized Learning Toward Data ScienceCode0
Information-theoretic Task Selection for Meta-Reinforcement Learning—0
Fast Reinforcement Learning with Incremental Gaussian Mixture Models—0
Cooperative Heterogeneous Deep Reinforcement Learning—0
Instance based Generalization in Reinforcement LearningCode0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix—0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Reinforcement Learning with Efficient Active Feature Acquisition—0
Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms—0
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation—0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases—0
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network—0
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Benchmark Results

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