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 13551–13600 of 15113 papers

TitleStatusHype
End-to-End Race Driving with Deep Reinforcement Learning—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks—0
Goal-oriented Trajectories for Efficient Exploration—0
Arcades: A deep model for adaptive decision making in voice controlled smart-home—0
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control—0
Transfer with Model Features in Reinforcement Learning—0
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion—0
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation—0
Region Growing Curriculum Generation for Reinforcement Learning—0
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning—0
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
A Reinforcement Learning Neural Network for Robotic Manipulator Control—0
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated CurlingCode0
Learning to Act in Decentralized Partially Observable MDPs—0
Beyond the One-Step Greedy Approach in Reinforcement Learning—0
Learning to Explore via Meta-Policy Gradient—0
Policy and Value Transfer in Lifelong Reinforcement Learning—0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningCode0
Policy Optimization with Demonstrations—0
Mix & Match - Agent Curricula for Reinforcement Learning—0
Understanding and Simplifying One-Shot Architecture Search—0
Spotlight: Optimizing Device Placement for Training Deep Neural Networks—0
State Abstractions for Lifelong Reinforcement Learning—0
Feudal Dialogue Management with Jointly Learned Feature Extractors—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
A Language Model based Evaluator for Sentence Compression—0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Deep Reinforcement Learning for NLP—0
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence ArchitecturesCode0
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis—0
Learning to Drive in a DayCode0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning—0
Hierarchical Reinforcement Learning with Abductive Planning—0
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level GenerationCode0
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic ManipulationCode0
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning—0
Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games—0
Deep Generative Models with Learnable Knowledge Constraints—0
Accuracy-based Curriculum Learning in Deep Reinforcement LearningCode0
A Tour of Reinforcement Learning: The View from Continuous ControlCode0
DARTS: Differentiable Architecture SearchCode1
Deep Reinforcement Learning: An Overview—0
Many-Goals Reinforcement Learning—0
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning—0
A New Approach for Resource Scheduling with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Surgical Gesture Segmentation and ClassificationCode0
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning ExperimentsCode0
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Benchmark Results

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