Browsing by Subject "Reinforcement learning"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item “Design of Stock Trading Agent Using Deep Reinforcement Learning”(IOE Pulchowk Campus, 2022-09) Lal, Janak KumarThis study adopts Double Deep Q learning algorithm to design trading strategies to trade stocks of four commercial banks listed in NEPSE. The reinforcement learning agent takes discrete actions and gets negative or positive reward from the environment. CNN is utilized to form the policy network. A target network is used to mitigate instability due to Deep Q Network. The concept of experience replay is used to randomly sample the batches of experience from the memory and train the network. The performance of Double Deep Q learning agent was compared with various baseline trading strategies in terms of annualised expected trade return. The maximum annualised expected trade return obtained with traditional baseline methods was 103% for testing data of NABIL, while for the same data the reinforcement learning agent using double deep Q learning algorithm obtained annualized expected trade return of 114.44%. The experiments showed that, Double Deep Q learning agent with experience replay had higher annualised expected trade return compared to baseline trading strategies.Item Self managed cloud computing and edge computing system using deep reinforcement learning(Pulchowk Campus, 2021-09) Shakya, SushilIn order to tackle the latency problems in sensitive applications implemented in IoT devices, the edge computing came into existence. On top of that, the idea of a mobile edge computing (MEC) network, in which such computationally heavy activities are computed in many edge servers deployed adjacent to mobile devices, has recently gained popularity. Unlike cloud server, the edge device has a nite computation capacity and it can't handle massive computation tasks. There needs to be a smart task o oading scheme in order to decide whether to execute the task in the local device itself, or o oad it to the edge device and process there or again send it further to the cloud server for processing. Also the algorithm should be able to utilize the available network bandwidth e ciently. The optimization of joint o oading decision and bandwidth allocation in a multi user, multi task, multi server environment can be formulated as a mixed integer non linear programming problem (MINLP) in MEC to preserve energy and maintain quality of service for wireless devices. MINLP is a NP-hard problem and the time complexity to solve it grows exponentially. In this research work, the power of deep learning and reinforcement learning has been applied to solve this MINLP problem under a fraction of a second which makes it suitable for real world usage. Further an end to end integrated edge and cloud computing system has been proposed that switches from one to another whenever required, and leverages the bene ts of both paradigm.