scholarly journals A study on handling intrinsic motivation for devising sample efficient actor-critic agents

2021 ◽  
Author(s):  
André Quadros ◽  
Roberto Xavier Junior ◽  
Kleber Souza ◽  
Bruno Gomes ◽  
Filipe Saraiva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Reinforcement Learning ◽  
Intrinsic Motivation ◽  
Sampling Efficiency ◽  
Practical Guidelines

Reinforcement learning has evolved in recent years,and overcoming challenges found in this field. This area, unlikeconventional machine learning, does not learn through a setof observational instances, but through interaction with anenvironment. The sampling efficiency of a reinforcement learningagent is a challenge. That is, how to make an agent learn withinan environment with as little interaction as possible. In this workwe perform an experimental study on the difficulties to integratea strategy of intrinsic motivation to an actor-critic agent toimprove the sampling efficiency. We found results that point to theeffectiveness of the intrinsic motivation as a approach to improvethe agent’s sampling efficiency, as well as its performance. Weshare practical guidelines to assist in the implementation of actor-critic agents to deal with sparse reward environments whilemaking use of intrinsic motivation feedback.

scholarly journals Recommending System for Penny Stock Trading

2019 ◽  
Vol 9 (1S5) ◽  
pp. 85-91
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Reinforcement Learning ◽  
Test Data ◽  
Stock Price ◽  
Prediction Models ◽  
Poor Performance ◽  
Stock Trading ◽  
Financial Metrics ◽  
Penny Stocks

Penny stocks at times makes the investors wealthy by turning to be a multi-bagger stocks or erode the wealth of the investors with poor performance in volatile conditions. While there are many machine learning-based prediction models that are used for stock price evaluation, very few studies have focused on the dynamics to be considered in penny stock conditions. Though the pattern might remain the same for normal stocks and the penny stock classification, still some of the parameters to be evaluated in the process needs changes. The model discussed in this report is a comprehensive solution discussed as scope for evaluation of the penny stock pick, using trading and reporting financial metrics. Experimental study of the test data indicates that the model is potential and if can be used effectively with reinforcement learning pattern, it can turn to be sustainable solution.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Quantum Reinforcement Learning with Quantum Photonics

Photonics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 33
Author(s):  
Lucas Lamata
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Quantum Computation ◽  
Exchange Information ◽  
Quantum Machine Learning ◽  
Quantum Technology ◽  
Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

Modeling fine-scale residential land price distribution: An experimental study using open data and machine learning

2021 ◽  
Vol 129 ◽  
pp. 102442
Author(s):  
Peng Zhang ◽  
Shougeng Hu ◽  
Weidong Li ◽  
Chuanrong Zhang ◽  
Shengfu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Open Data ◽  
Fine Scale ◽  
Land Price ◽  
Residential Land ◽  
Price Distribution

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals Applying a Deep Q Network for OpenAIs Car Racing Game

2020 ◽  
Author(s):  
Ali Fakhry
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Techniques ◽  
Car Racing ◽  
Custom Made ◽  
Learning Technique ◽  
Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

scholarly journals Temporal Uncertainty During Overshadowing

2011 ◽  
pp. 46-55
Author(s):  
Dómhnall J. Jennings ◽  
Eduardo Alonso ◽  
Esther Mondragón ◽  
Charlotte Bonardi
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Associative Learning ◽  
Learning Theories ◽  
Temporal Difference ◽  
Temporal Uncertainty ◽  
Learning Problem ◽  
Difference Model ◽  
Distribution Form ◽  
Temporal Properties

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

scholarly journals Label Enhancement for Label Distribution Learning via Prior Knowledge

2020 ◽  
Author(s):  
Yongbiao Gao ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Emotion Recognition ◽  
Prior Knowledge ◽  
Decision Process ◽  
Age Estimation ◽  
State Of The Art ◽  
Learning Agent ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to an instance. However, most of training datasets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. We propose to use the prior knowledge to recover the label distributions. The process of recovering the label distributions from the logical labels is called label enhancement. In this paper, we formulate the label enhancement as a dynamic decision process. Thus, the label distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

Sign in / Sign up

Export Citation Format

Share Document