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 14701–14750 of 15113 papers

TitleStatusHype
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Quantum-enhanced machine learning—0
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles—0
Utilization of Deep Reinforcement Learning for saccadic-based object visual search—0
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies—0
A Reinforcement Learning Approach to the View Planning Problem—0
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
Sim-to-Real Robot Learning from Pixels with Progressive Nets—0
Reset-free Trial-and-Error Learning for Robot Damage RecoveryCode0
Introduction to the "Industrial Benchmark"—0
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation—0
Personalizing a Dialogue System with Transfer Reinforcement Learning—0
Multi-Objective Deep Reinforcement LearningCode0
Deep Reinforcement Learning From Raw Pixels in Doom—0
Active exploration in parameterized reinforcement learningCode0
Connecting Generative Adversarial Networks and Actor-Critic Methods—0
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots—0
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States—0
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates—0
Deep Visual Foresight for Planning Robot MotionCode0
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search—0
Deep Reinforcement Learning for Tensegrity Robot Locomotion—0
Deep Reinforcement Learning for Mention-Ranking Coreference ModelsCode0
UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS—0
Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game—0
Input Convex Neural NetworksCode0
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer—0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]—0
Towards Deep Symbolic Reinforcement Learning—0
Opponent Modeling in Deep Reinforcement LearningCode0
Playing FPS Games with Deep Reinforcement LearningCode0
SeqGAN: Sequence Generative Adversarial Nets with Policy GradientCode0
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement LearningCode0
The Option-Critic ArchitectureCode0
Exploration Potential—0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning—0
Bayesian Reinforcement Learning: A Survey—0
Stochastic evolution in populations of ideas—0
A Threshold-based Scheme for Reinforcement Learning in Neural NetworksCode0
A centralized reinforcement learning method for multi-agent job scheduling in Grid—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Unifying task specification in reinforcement learning—0
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information AccessCode0
Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning—0
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
Modeling Human Reading with Neural Attention—0
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Benchmark Results

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