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Found 14 papers, 3 papers with code
How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples
However, surveying existing image-based methods, we noticed a need for more human evaluations of the proposed image modifications.
Data Valuation for Offline Reinforcement Learning
The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions.
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A comparative evaluation of machine learning methods for robot navigation through human crowds
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety.
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
In this paper, we demonstrate how data from videos of human running (e. g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result.
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
Movement control of artificial limbs has made big advances in recent years.
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell.
Curriculum Learning with a Progression Function
Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed.
Graph-based State Representation for Deep Reinforcement Learning
Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL.
Uniform State Abstraction For Reinforcement Learning
Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinforcement Learning.
Generating Stereotypes Automatically For Complex Categorical Features
In the context of stereotypes creation for recommender systems, we found that certain types of categorical variables pose particular challenges if simple clustering procedures were employed with the objective to create stereotypes.
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems
These are learning time, scalability and decentralised coordination i. e. no communication between the learning agents.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
