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 8826–8850 of 15113 papers

TitleStatusHype
Reverb: A Framework For Experience ReplayCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Learning State Representations from Random Deep Action-conditional PredictionsCode0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
Introduction to Machine Learning for the Sciences—0
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
Generate and Revise: Reinforcement Learning in Neural Poetry—0
Learning Optimal Strategies for Temporal Tasks in Stochastic Games—0
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature—0
Neurogenetic Programming Framework for Explainable Reinforcement LearningCode0
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning WorkloadsCode1
Unlocking Pixels for Reinforcement Learning via Implicit Attention—0
Sparsely ensembled convolutional neural network classifiers via reinforcement learningCode0
An Analysis of Frame-skipping in Reinforcement Learning—0
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking—0
LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle ControlCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Improving Model and Search for Computer Go—0
A bandit approach to curriculum generation for automatic speech recognition—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
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Benchmark Results

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