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 13801–13850 of 15113 papers

TitleStatusHype
Dyna Planning using a Feature Based Generative Model—0
Deep Reinforcement Learning of Marked Temporal Point ProcessesCode0
Discovering Blind Spots in Reinforcement Learning—0
Reinforcement Learning for Heterogeneous Teams with PALO Bounds—0
Scalable Coordinated Exploration in Concurrent Reinforcement LearningCode0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied AgentsCode0
Data-Efficient Hierarchical Reinforcement LearningCode0
A General Family of Robust Stochastic Operators for Reinforcement Learning—0
Evolution-Guided Policy Gradient in Reinforcement LearningCode0
Learning Safe Policies with Expert Guidance—0
A Framework and Method for Online Inverse Reinforcement Learning—0
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation—0
Hierarchical Reinforcement Learning with Hindsight—0
Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning—0
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from BehaviorCode0
Unsupervised Video Object Segmentation for Deep Reinforcement LearningCode0
Machine Teaching for Inverse Reinforcement Learning: Algorithms and ApplicationsCode0
Constrained Policy Improvement for Safe and Efficient Reinforcement LearningCode0
Learning to Teach in Cooperative Multiagent Reinforcement Learning—0
A Lyapunov-based Approach to Safe Reinforcement LearningCode0
Learning Real-World Robot Policies by Dreaming—0
Episodic Memory Deep Q-Networks—0
Reinforcement Learning of Theorem Proving—0
Solving the Rubik's Cube Without Human KnowledgeCode0
Two geometric input transformation methods for fast online reinforcement learning with neural nets—0
Improving Image Captioning with Conditional Generative Adversarial NetsCode0
Hierarchical Reinforcement Learning with Deep Nested Agents—0
Evolutionary RL for Container Loading—0
Learning Time-Sensitive Strategies in Space FortressCode0
Language Expansion In Text-Based Games—0
Deep Reinforcement Learning for Resource Management in Network Slicing—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning—0
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning—0
The Hierarchical Adaptive Forgetting Variational Filter—0
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects—0
Do deep reinforcement learning agents model intentions?Code0
Graph Signal Sampling via Reinforcement Learning—0
Feedback-Based Tree Search for Reinforcement Learning—0
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning ApproachCode0
GAN Q-learningCode0
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery—0
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture—0
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization—0
Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes—0
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
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Benchmark Results

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