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 12401–12450 of 15113 papers

TitleStatusHype
Benchmarking Model-Based Reinforcement LearningCode0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation—0
Perspective Taking in Deep Reinforcement Learning Agents—0
Reasoning and Generalization in RL: A Tool Use PerspectiveCode0
On the Weaknesses of Reinforcement Learning for Neural Machine Translation—0
Playing Go without Game Tree Search Using Convolutional Neural Networks—0
Modified Actor-Critics—0
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
Generalizing from a few environments in safety-critical reinforcement learning—0
Dynamic Face Video Segmentation via Reinforcement Learning—0
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem—0
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree PolicyCode0
Historical Text Normalization with Delayed Rewards—0
Look Harder: A Neural Machine Translation Model with Hard Attention—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
End-to-end Deep Reinforcement Learning Based Coreference Resolution—0
Designing Deep Reinforcement Learning for Human Parameter Exploration—0
Learning How to Active Learn by DreamingCode0
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning—0
Reinforced Training Data Selection for Domain Adaptation—0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation—0
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable ModelCode0
Multiple Landmark Detection using Multi-Agent Reinforcement LearningCode0
Variational Quantum Circuits for Deep 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
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes—0
From self-tuning regulators to reinforcement learning and back again—0
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction—0
Hyp-RL : Hyperparameter Optimization by Reinforcement LearningCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Toward Simulating Environments in Reinforcement Learning Based Recommendations—0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Compositional Transfer in Hierarchical Reinforcement Learning—0
PyRep: Bringing V-REP to Deep Robot LearningCode0
Towards Empathic Deep Q-LearningCode0
Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning—0
Approximate Dynamic Programming For Linear Systems with State and Input Constraints—0
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning—0
Cooperation-Aware Reinforcement Learning for Merging in Dense TrafficCode0
Policy Optimization with Stochastic Mirror Descent—0
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy—0
Optimistic Proximal Policy Optimization—0
On Multi-Agent Learning in Team Sports Games—0
Probabilistic model predictive safety certification for learning-based control—0
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Benchmark Results

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