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Found 33 papers, 1 papers with code
Continually Learned Pavlovian Signalling Without Forgetting for Human-in-the-Loop Robotic Control
One challenge identified in these learning methods is that they can forget previously learned predictions when a user begins to successfully act upon delivered feedback.
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb.
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have
As a second main contribution of this work, we show how these properties may be implemented together in a proof-of-concept reinforcement learning agent: we demonstrate how the properties manifest in the behaviour of this agent in a simple non-episodic grid-world environment that includes curiosity-inducing locations and induced targets of curiosity.
Adaptive patch foraging in deep reinforcement learning agents
Patch foraging is one of the most heavily studied behavioral optimization challenges in biology.
The Alberta Plan for AI Research
Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan.
What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about environment observations are used as additional input features to enable better goal-directed decision-making.
Prototyping three key properties of specific curiosity in computational reinforcement learning
Curiosity for machine agents has been a focus of intense research.
A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning
Two major open research questions in the field of IML are: "How should we design systems that can learn to make better decisions over time with human interaction?"
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently.
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals.
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study
Additionally, we compare two different agent architectures to assess how representational choices in agent design affect the human-agent interaction.
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations.
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
In this article we argue for the domain of voice document editing and for the methods of model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
What's a Good Prediction? Challenges in evaluating an agent's knowledge
Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems.
Gamma-Nets: Generalizing Value Estimation over Timescale
By using the timescale as one of the estimator's inputs we can estimate value for arbitrary timescales.
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation.
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
In this paper, we examine the utility of extending PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight how they can be a scalable and interpretable option for control systems.
Learned human-agent decision-making, communication and joint action in a virtual reality environment
In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task.
When is a Prediction Knowledge?
While promising, we here suggest that the notion of predictions as knowledge in reinforcement learning is as yet underdeveloped: some work explicitly refers to predictions as knowledge, what the requirements are for considering a prediction to be knowledge have yet to be well explored.
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
In this paper, we examine an instance of meta-learning in which feature relevance is learned by adapting step size parameters of stochastic gradient descent---building on a variety of prior work in stochastic approximation, machine learning, and artificial neural networks.
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning.
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation
As a primary contribution of this work, we show that using SR-based predictions can improve sample efficiency and learning speed in a continual learning setting where new predictions are incrementally added and learned over time.
Reactive Reinforcement Learning in Asynchronous Environments
The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored.
Communicative Capital for Prosthetic Agents
This work presents an overarching perspective on the role that machine intelligence can play in enhancing human abilities, especially those that have been diminished due to injury or illness.
Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback
In many human-machine interaction settings, there is a growing gap between the degrees-of-freedom of complex semi-autonomous systems and the number of human control channels.
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
We illustrate the impact of varying human feedback parameters on task performance by investigating the probability of giving feedback on each time step and the likelihood of given feedback being correct.
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning
This paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement learning robotic agent.
Introspective Agents: Confidence Measures for General Value Functions
Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions.
Face valuing: Training user interfaces with facial expressions and reinforcement learning
The primary objective of the current work is to demonstrate that a learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions---we call this face valuing.
True Online Temporal-Difference Learning
Our results suggest that the true online methods indeed dominate the regular methods.
An Empirical Evaluation of True Online TD(λ)
Our results confirm the strength of true online TD({\lambda}): 1) for sparse feature vectors, the computational overhead with respect to TD({\lambda}) is minimal; for non-sparse features the computation time is at most twice that of TD({\lambda}), 2) across all domains/representations the learning speed of true online TD({\lambda}) is often better, but never worse than that of TD({\lambda}), and 3) true online TD({\lambda}) is easier to use, because it does not require choosing between trace types, and it is generally more stable with respect to the step-size.
Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings
Our study therefore contributes initial evidence that prediction learning and machine intelligence can benefit not just control, but also feedback from an artificial limb.
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb
In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm.
