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 14851–14900 of 15113 papers

TitleStatusHype
Learning to Compose Neural Networks for Question AnsweringCode0
Angrier Birds: Bayesian reinforcement learningCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Inverse Reinforcement Learning via Deep Gaussian Process—0
Deep Reinforcement Learning in Large Discrete Action SpacesCode0
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments—0
Increasing the Action Gap: New Operators for Reinforcement LearningCode0
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies—0
Deep Attention Recurrent Q-NetworkCode0
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria—0
Q-Networks for Binary Vector Actions—0
State of the Art Control of Atari Games Using Shallow Reinforcement LearningCode0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
Inverse Reinforcement Learning with Locally Consistent Reward Functions—0
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models—0
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice—0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning—0
On the convergence of cycle detection for navigational reinforcement learning—0
Strategic Dialogue Management via Deep Reinforcement LearningCode0
MazeBase: A Sandbox for Learning from GamesCode0
Dueling Network Architectures for Deep Reinforcement LearningCode0
Conditional Computation in Neural Networks for faster modelsCode0
Actor-Mimic: Deep Multitask and Transfer Reinforcement LearningCode0
Policy DistillationCode0
Active Object Localization with Deep Reinforcement LearningCode0
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control—0
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning—0
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective—0
Generating Text with Deep Reinforcement Learning—0
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning—0
On the Computability of AIXI—0
Dual Control for Approximate Bayesian Reinforcement Learning—0
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models—0
Variational Information Maximisation for Intrinsically Motivated Reinforcement LearningCode0
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors—0
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration—0
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search—0
Deep Spatial Autoencoders for Visuomotor LearningCode0
Benchmarking for Bayesian Reinforcement Learning—0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning—0
Recurrent Reinforcement Learning: A Hybrid Approach—0
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies—0
Reinforcement Learning with Parameterized ActionsCode0
Reinforcement Learning in Multi-Party Trading Dialog—0
Reinforcement Learning of Multi-Issue Negotiation Dialogue Policies—0
Optimising Turn-Taking Strategies With Reinforcement Learning—0
Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management—0
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers—0
Learning Efficient Representations for Reinforcement Learning—0
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Benchmark Results

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