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 12251–12300 of 15113 papers

TitleStatusHype
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
Modified Actor-Critics—0
Generalizing from a few environments in safety-critical reinforcement learning—0
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem—0
Dynamic Face Video Segmentation via Reinforcement Learning—0
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree PolicyCode0
Learning How to Active Learn by DreamingCode0
Look Harder: A Neural Machine Translation Model with Hard Attention—0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation—0
Reinforced Training Data Selection for Domain Adaptation—0
Historical Text Normalization with Delayed Rewards—0
End-to-end Deep Reinforcement Learning Based Coreference Resolution—0
Designing Deep Reinforcement Learning for Human Parameter Exploration—0
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning—0
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control—0
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable ModelCode0
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning—0
Variational Quantum Circuits for Deep Reinforcement LearningCode0
Multiple Landmark Detection using Multi-Agent Reinforcement LearningCode0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning—0
Detecting Spiky Corruption in Markov Decision ProcessesCode0
On Training Flexible Robots using Deep Reinforcement Learning—0
Growing Action SpacesCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Hyp-RL : Hyperparameter Optimization by Reinforcement LearningCode0
From self-tuning regulators to reinforcement learning and back again—0
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes—0
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction—0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Toward Simulating Environments in Reinforcement Learning Based Recommendations—0
Compositional Transfer in Hierarchical Reinforcement Learning—0
PyRep: Bringing V-REP to Deep Robot LearningCode0
Towards Empathic Deep Q-LearningCode0
Approximate Dynamic Programming For Linear Systems with State and Input Constraints—0
Cooperation-Aware Reinforcement Learning for Merging in Dense TrafficCode0
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning—0
Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning—0
Probabilistic model predictive safety certification for learning-based control—0
Policy Optimization with Stochastic Mirror Descent—0
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives—0
On Multi-Agent Learning in Team Sports Games—0
Optimistic Proximal Policy Optimization—0
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy—0
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy AnalysisCode1
Event-Driven Models—0
A Theoretical Connection Between Statistical Physics and Reinforcement Learning—0
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals—0
Deep Conservative Policy Iteration—0
Inverse reinforcement learning conditioned on brain scan—0
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Benchmark Results

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