Reinforcement learning has become a powerful learning framework now capable of learning complex policies in high dimensional environments. At every time step t, the agent observes the current state of the environment, sₜ , and uses the policy it is learning π(s), to inform a decision about which action, aₜ , it should take if it wishes to maximise the total reward in the current episode.

Core concept of reinforcement learning.

In this article, I will be introducing cooperative multi-agent reinforcement learning and how it is applicable in the autonomous driving problem. I will also be providing a multi-agent training environment which is adapted from…

Ethereum is currently the most widely adopted public blockchain for decentralised applications development. I found that most information on the internet regarding Ethereum development is slightly fragmented. Therefore, in this article I would like to provide a comprehensive overview on getting started with programming on Ethereum, from setting up tools to deploying your smart contract. If you are a complete beginner and would like to learn about Ethereum, you are now at the right place. If you are already learning Ethereum development and got stuck somewhere, this article might give you a better overview.

First of all, we need to…

The use of reinforcement learning (RL) to build a stock trading agent has been a widely explored topic. Some argue that an RL agent would not be able to reliably make good decisions in the stock market, as the market is highly volatile in nature and depends on multiple external factors such as behavourial finance and market manipulation. With that in mind, RL in trading could only be classified as a semi Markov Decision Process (the outcome is not solely based on the previous state and your action, it also depends on other traders).

In this article, I would like…

Bidirectional Encoder Representations from Transformers (BERT) is a major advancement in the field of Natural Language Processing (NLP) in recent years. BERT achieves good performances in many aspects of NLP, such as text classification, text summarisation and question answering.

In this article, I will walk through how to fine tune a BERT model based on your own dataset to do text classification (sentiment analysis in my case). When browsing through the net to look for guides, I came across mostly PyTorch implementation or fine-tuning using pre-existing dataset such as the GLUE dataset. …

Kai Jun Eer

Wandering in the world of artificial intelligence and blockchain

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