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Found 15 papers, 2 papers with code
Differential Recurrent Neural Networks for Action Recognition
This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN).
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.
Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks
In this paper, we introduce a new incremental learning algorithm called crossprop, which learns incoming weights of hidden units based on the meta-gradient descent approach, that was previously introduced by Sutton (1992) and Schraudolph (1999) for learning step-sizes.
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.
Many-Goals Reinforcement Learning
Second, we explore whether many-goals updating can be used to pre-train a network to subsequently learn faster and better on a single main task of interest.
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.
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions.
How Should an Agent Practice?
We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available.
A Self-Tuning Actor-Critic Algorithm
Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify hyperparameters that perform well on a new domain.
Learning Retrospective Knowledge with Reverse Reinforcement Learning
We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge.
Learning State Representations from Random Deep Action-conditional Predictions
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i. e., deep action-conditional predictions -- random both in what feature of observations they predict as well as in the sequence of actions the predictions are conditioned upon -- form good auxiliary tasks for reinforcement learning (RL) problems.
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.
GrASP: Gradient-Based Affordance Selection for Planning
Our empirical work shows that it is feasible to learn to select both primitive-action and option affordances, and that simultaneously learning to select affordances and planning with a learned value-equivalent model can outperform model-free RL.
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Such applications often require to put constraints on the agent's behavior.
Diversifying AI: Towards Creative Chess with AlphaZero
In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones.
