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 14301–14350 of 15113 papers

TitleStatusHype
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models—0
Guided Deep Reinforcement Learning for Swarm SystemsCode0
Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
Improving Search through A3C Reinforcement Learning based Conversational Agent—0
Closing the loop between neural network simulators and the OpenAI Gym—0
Deep Reinforcement Learning for Conversational AICode0
Shapechanger: Environments for Transfer LearningCode0
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning—0
Unsupervised state representation learning with robotic priors: a robustness benchmark—0
Shared Learning : Enhancing Reinforcement in Q-Ensembles—0
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest—0
A2-RL: Aesthetics Aware Reinforcement Learning for Image CroppingCode0
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning—0
A Study of AI Population Dynamics with Million-agent Reinforcement Learning—0
Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging—0
Deep Reinforcement Learning with Surrogate Agent-Environment Interface—0
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds—0
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning—0
Autonomous Quadrotor Landing using Deep Reinforcement Learning—0
MBMF: Model-Based Priors for Model-Free Reinforcement Learning—0
Mirror Descent Search and its AccelerationCode0
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlowCode0
Ultimate Intelligence Part III: Measures of Intelligence, Perception and Intelligent Agents—0
Prosocial learning agents solve generalized Stag Hunts better than selfish onesCode0
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge—0
Approximating meta-heuristics with homotopic recurrent neural networks—0
A Deep Reinforcement Learning Chatbot—0
Towards Neural Machine Translation with Latent Tree Attention—0
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning—0
Learning what to read: Focused machine reading—0
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Mean Actor CriticCode0
Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous MethodsCode0
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments—0
Optimal and Learning Control for Autonomous Robots—0
Asymptotic Bias of Stochastic Gradient Search—0
Safe Reinforcement Learning via ShieldingCode0
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?Code0
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks—0
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning—0
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets—0
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method—0
A Brief Survey of Deep Reinforcement Learning—0
StarCraft II: A New Challenge for Reinforcement LearningCode0
Deep Reinforcement Learning for High Precision Assembly Tasks—0
Deep Object-Centric Representations for Generalizable Robot LearningCode0
Group-driven Reinforcement Learning for Personalized mHealth InterventionCode0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
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Benchmark Results

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