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 13651–13700 of 15113 papers

TitleStatusHype
Goal-oriented Trajectories for Efficient Exploration—0
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control—0
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation—0
Region Growing Curriculum Generation for Reinforcement Learning—0
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion—0
Transfer with Model Features in Reinforcement Learning—0
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning—0
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
Feudal Dialogue Management with Jointly Learned Feature Extractors—0
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated CurlingCode0
A Language Model based Evaluator for Sentence Compression—0
Learning to Act in Decentralized Partially Observable MDPs—0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Learning to Drive in a DayCode0
Beyond the One-Step Greedy Approach in Reinforcement Learning—0
Learning to Explore via Meta-Policy Gradient—0
Deep Reinforcement Learning for NLP—0
Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning—0
A Reinforcement Learning Neural Network for Robotic Manipulator Control—0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
Mix & Match - Agent Curricula for Reinforcement Learning—0
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence ArchitecturesCode0
Spotlight: Optimizing Device Placement for Training Deep Neural Networks—0
State Abstractions for Lifelong Reinforcement Learning—0
Policy and Value Transfer in Lifelong Reinforcement Learning—0
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis—0
Policy Optimization with Demonstrations—0
Understanding and Simplifying One-Shot Architecture Search—0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningCode0
Hierarchical Reinforcement Learning with Abductive Planning—0
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level GenerationCode0
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning—0
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic ManipulationCode0
Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games—0
Deep Generative Models with Learnable Knowledge Constraints—0
A Tour of Reinforcement Learning: The View from Continuous ControlCode0
Accuracy-based Curriculum Learning in Deep Reinforcement LearningCode0
Deep Reinforcement Learning: An Overview—0
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning—0
Many-Goals Reinforcement Learning—0
A New Approach for Resource Scheduling with Deep Reinforcement Learning—0
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning ExperimentsCode0
Deep Reinforcement Learning for Surgical Gesture Segmentation and ClassificationCode0
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning—0
Sim-to-Real Reinforcement Learning for Deformable Object ManipulationCode0
Reinforcement Learning using Augmented Neural Networks—0
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning—0
RUDDER: Return Decomposition for Delayed RewardsCode0
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Benchmark Results

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