Autism spectrum disorder (ASD) is a developmental disability that can impair communication, social skills, living skills, and learning capabilities. Learning approaches usually differ between mainstream schools and special needs schools, to cater for the different learning processes of children with ASD. Besides the traditional classroom-based education, alternative technology and methods are explored for special needs education. One method is to train children with ASD using Virtual Reality (VR) technologies. Many prior works show the effectiveness of VR-based learning with varying degrees of success. Some children with ASD may face challenges to gain independent living skills. Their parents or guardians have to expend a significant amount of effort in taking care of children with ASD. It will be very helpful if they can have a learning opportunity to gain such living skills. In this research, we develop a VR serious game to train children with ASD one of the basic living skills for road crossing safely. The VR serious game operates on multiple types of platforms, with various user interaction inputs including the Microsoft Kinect sensor, keyboard, mouse, and touch screen. The game design and methodology will be described in this paper. Experiments have been conducted to evaluate the learning effectiveness of the road crossing game, with very positive results achieved in the quiz and survey questionnaire after the gameplay.
Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.
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