Search Results for author:
Found 8 papers, 3 papers with code
A general class of surrogate functions for stable and efficient reinforcement learning
Common policy gradient methods rely on the maximization of a sequence of surrogate functions.
Hierarchical Representation Learning for Markov Decision Processes
In this paper we present a novel method for learning hierarchical representations of Markov decision processes.
Hierarchical reinforcement learning for efficient exploration and transfer
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time.
Efficient Exploration
Hierarchical Reinforcement Learning
+2
Hierarchical reinforcement learning for efficent exploration and transfer
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
We propose a novel model based reinforcement learning algorithm that is able to address both types of changes.
Model-based Reinforcement Learning
reinforcement-learning
+1
Adaptive Smoothing Path Integral Control
We identify PICE as the infinite smoothing limit of such technique and show that the sample efficiency problems that PICE suffers disappear for finite levels of smoothing.
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data.
Kafnets: kernel-based non-parametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions.
