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

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Showing 12251–12300 of 15113 papers

TitleStatusHype
Automated quantum programming via reinforcement learning for combinatorial optimizationCode0
Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence—0
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control—0
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access—0
ARAML: A Stable Adversarial Training Framework for Text GenerationCode0
Reinforcement Learning is not a Causal problem—0
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design—0
A survey on intrinsic motivation in reinforcement learning—0
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement LearningCode0
Transfer in Deep Reinforcement Learning using Knowledge Graphs—0
Reinforcement Learning Applications—0
Mitigating Multi-Stage Cascading Failure by Reinforcement Learning—0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
A model of discrete choice based on reinforcement learning under short-term memory—0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Deep reinforcement learning in World-Earth system models to discover sustainable management strategiesCode0
Playing a Strategy Game with Knowledge-Based Reinforcement Learning—0
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning—0
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability—0
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning—0
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction—0
Model-based Lookahead Reinforcement Learning—0
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question GenerationCode0
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity—0
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point ProcessCode0
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real—0
Reinforcement Learning based Interconnection Routing for Adaptive Traffic OptimizationCode0
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective—0
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility—0
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing fieldCode0
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA SystemCode0
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
A review on Deep Reinforcement Learning for Fluid MechanicsCode0
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games—0
A Review of Cooperative Multi-Agent Deep Reinforcement Learning—0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Behaviour Suite for Reinforcement LearningCode0
Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach—0
Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods—0
Vision-based Navigation Using Deep Reinforcement LearningCode0
Free-Lunch Saliency via Attention in Atari AgentsCode0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction—0
DoorGym: A Scalable Door Opening Environment And Baseline AgentCode0
Reusability and Transferability of Macro Actions for Reinforcement Learning—0
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Benchmark Results

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