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 12101–12150 of 15113 papers

TitleStatusHype
Reinforcement Learning Applications—0
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual NavigationCode1
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
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning—0
Secure Computation Offloading in Blockchain based IoT Networks with Deep 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
Model-based Lookahead Reinforcement Learning—0
Playing a Strategy Game with Knowledge-Based Reinforcement Learning—0
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability—0
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction—0
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point ProcessCode0
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity—0
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question GenerationCode0
Reinforcement Learning based Interconnection Routing for Adaptive Traffic OptimizationCode0
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective—0
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real—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
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking—0
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA SystemCode0
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
A review on Deep Reinforcement Learning for Fluid MechanicsCode0
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
Vision-based Navigation Using Deep 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
Free-Lunch Saliency via Attention in Atari AgentsCode0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
DoorGym: A Scalable Door Opening Environment And Baseline AgentCode0
Reusability and Transferability of Macro Actions for Reinforcement Learning—0
Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction—0
Dueling Posterior Sampling for Preference-Based Reinforcement LearningCode0
A View on Deep Reinforcement Learning in System Optimization—0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Improving Deep Reinforcement Learning in Minecraft with Action Advice—0
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control—0
Reinforcement Learning for Personalized Dialogue Management—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Optimal Attacks on Reinforcement Learning Policies—0
Show:102550
← PrevPage 243 of 303Next →

Benchmark Results

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