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 13951–14000 of 15113 papers

TitleStatusHype
Setting up a Reinforcement Learning Task with a Real-World RobotCode0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Automated Curriculum Learning by Rewarding Temporally Rare EventsCode0
Neural Text Generation: Past, Present and Beyond—0
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning—0
Measurement-based adaptation protocol with quantum reinforcement learning—0
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning—0
Imitation Learning with Concurrent Actions in 3D Games—0
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies—0
Learning to Explore with Meta-Policy Gradient—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Policy Search in Continuous Action Domains: an Overview—0
Soft-Robust Actor-Critic Policy-Gradient—0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
Kickstarting Deep Reinforcement Learning—0
Variance Networks: When Expectation Does Not Meet Your ExpectationsCode0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
Feudal Reinforcement Learning for Dialogue Management in Large Domains—0
A Multi-Objective Deep Reinforcement Learning Framework—0
A Brandom-ian view of Reinforcement Learning towards strong-AI—0
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning—0
Intent-aware Multi-agent Reinforcement Learning—0
Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning—0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement LearningCode0
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs—0
OIL: Observational Imitation Learning—0
Some Considerations on Learning to Explore via Meta-Reinforcement LearningCode0
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and ApplicationCode0
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning—0
Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach—0
On Oracle-Efficient PAC RL with Rich Observations—0
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling—0
Deep Reinforcement Learning for Sponsored Search Real-time Bidding—0
Hierarchical Imitation and Reinforcement Learning—0
Deep Reinforcement Learning for Join Order Enumeration—0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Learning by Playing - Solving Sparse Reward Tasks from ScratchCode0
Model-Ensemble Trust-Region Policy OptimizationCode0
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
The Mirage of Action-Dependent Baselines in Reinforcement LearningCode0
DiGrad: Multi-Task Reinforcement Learning with Shared Actions—0
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for ResearchCode0
Variance Reduction Methods for Sublinear Reinforcement Learning—0
Reinforcement and Imitation Learning for Diverse Visuomotor SkillsCode0
Temporal Difference Models: Model-Free Deep RL for Model-Based Control—0
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Benchmark Results

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