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 14901–14950 of 15113 papers

TitleStatusHype
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Distributed Deep Q-Learning—0
Action-Conditional Video Prediction using Deep Networks in Atari GamesCode0
A Reinforcement Learning Approach to Online Learning of Decision Trees—0
Reinforcement Learning for the Unit Commitment Problem—0
Maximum Entropy Deep Inverse Reinforcement LearningCode0
Massively Parallel Methods for Deep Reinforcement LearningCode0
On the Computability of Solomonoff Induction and Knowledge-Seeking—0
Experimental analysis of data-driven control for a building heating system—0
Incentivizing Exploration In Reinforcement Learning With Deep Predictive ModelsCode0
Online Transfer Learning in Reinforcement Learning Domains—0
Bootstrapped Thompson Sampling and Deep Exploration—0
Language Understanding for Text-based Games Using Deep Reinforcement LearningCode0
Bootstrapping Skills—0
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning—0
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control—0
A Framework for Constrained and Adaptive Behavior-Based Agents—0
Local Nonstationarity for Efficient Bayesian Optimization—0
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret—0
A Definition of Happiness for Reinforcement Learning Agents—0
Reinforcement Learning applied to Single Neuron—0
Learning Where to Sample in Structured PredictionCode0
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach—0
Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning—0
Reinforcement Learning Neural Turing Machines - RevisedCode0
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning—0
Residential Demand Response Applications Using Batch Reinforcement Learning—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Human level control through deep reinforcement learningCode0
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP—0
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Efficient model-based reinforcement learning for approximate online optimal—0
From Pixels to Torques: Policy Learning with Deep Dynamical Models—0
Multiple Object Recognition with Visual AttentionCode0
Regression with Linear Factored Functions—0
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks—0
Sparse Multi-Task Reinforcement Learning—0
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning—0
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning—0
Difference of Convex Functions Programming for Reinforcement Learning—0
Design Principles of the Hippocampal Cognitive Map—0
How hard is my MDP?" The distribution-norm to the rescue"—0
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images—0
Compress and Control—0
A Comparison of learning algorithms on the Arcade Learning Environment—0
Do Artificial Reinforcement-Learning Agents Matter Morally?—0
Domain-Independent Optimistic Initialization for Reinforcement Learning—0
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
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Benchmark Results

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