A Portfolio Model with Risk Control Policy Based on Deep Reinforcement Learning
نویسندگان
چکیده
It was shown that deep reinforcement learning (DRL) has the potential to solve portfolio management problems in recent years. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is an actor-critic method, a typical DRL method continuous action space. Despite success of financial trading, surprisingly, most literature ignores element risk control. research proposed combine long- and short-term (LSTR) control with TD3 build model capabilities. Using Chinese stock data from Shanghai Stock Exchange, we train validate model. Performances were compared without results indicated our proposal offers better investment returns.
منابع مشابه
Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملStructure Learning in Motor Control: A Deep Reinforcement Learning Model
Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure. Although this ‘learning-to-learn’ effect is well documented, its underlying computational mechanisms are poorly understood. We present a new model of motor structure learning, approaching it from the point of vie...
متن کاملMulti-task learning with deep model based reinforcement learning
In recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. Most successful approaches focus on solving a single task, while multi-task reinforcement learning remains an open problem. In this paper, we present a model based approach to deep reinforcement learning which we use to solve different tasks simultaneousl...
متن کاملMelanoma detection with a deep learning model
Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions. Methods: In this analytic s...
متن کاملOn-Policy vs. Off-Policy Updates for Deep Reinforcement Learning
Temporal-difference-based deep-reinforcement learning methods have typically been driven by off-policy, bootstrap Q-Learning updates. In this paper, we investigate the effects of using on-policy, Monte Carlo updates. Our empirical results show that for the DDPG algorithm in a continuous action space, mixing on-policy and off-policy update targets exhibits superior performance and stability comp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11010019