mlpack 3: a fast, flexible machine learning library

Journal of Open Source Software 2018 1 code implementation

In the past several years, the field of machine learning has seen an explosion of interest and excitement, with hundreds or thousands of algorithms developed for different tasks every year.

Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping

18 Apr 20162 code implementations

With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks.

Financial Trading as a Game: A Deep Reinforcement Learning Approach

8 Jul 20181 code implementation

We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1.

Automated quantum programming via reinforcement learning for combinatorial optimization

21 Aug 20191 code implementation

Relative to a set of randomly generated problem instances, agents trained through reinforcement learning techniques are capable of producing short quantum programs which generate high quality solutions on both types of quantum resources.


A disembodied developmental robotic agent called Samu Bátfai

9 Nov 201511 code implementations

The basic objective of this paper is to reach the same results using reinforcement learning with general function approximators that can be achieved by using the classical Q lookup table on small input samples.


Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient

1 Jun 20181 code implementation

Convex composition optimization is an emerging topic that covers a wide range of applications arising from stochastic optimal control, reinforcement learning and multi-stage stochastic programming.

Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems

17 Apr 20191 code implementation

This paper proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, `open loop - closed loop', approach.

Meta-learning curiosity algorithms

ICLR 2020 1 code implementation

Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems.


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