Training open gym environment with continuous action space — So far so good, we have covered a bunch of exciting things in reinforcement learning till now ranging from basics to MAB, to Temporal Difference learning and plenty of Deep Reinforcement Learning algorithms namely REINFORCE, A2C, DQN, etc. You can check them out below in the Reinforcement Learning section

Understanding the maths behind the statistical concept with an example — If you have been in the Data Science space, you must have heard of the term ‘confidence intervals’, especially when you are estimating some numbers. Before we jump onto a practical example, it’s better we know a couple of statistical concepts Population: All the samples that represent a certain…

understanding the model architecture and training methodology used behind the ultimate chatbot — Everyone is talking about just one thing for a few months now i.e. ChatGPT, the powerful chatbot that is giving answers to all sorts of queries. But so far, most folks have focussed on using it as a black box model. In this post, I will try to unveil this…

webChatGPT, ChatSonic, Jupyter-AI assistant, Summarize and Talk-to-ChatGPT — As you must be knowing by now, ChatGPT is a state-of-the-art AI language model that can help you generate text, answer questions, and even write essays that have taken the world by storm. But did you know that there are browser extensions also that can help you harness the power…

Combining DQNs and REINFORCE algorithm for training agents — So in my previous posts, we have discussed the following Reinforcement Learning concepts Reinforcement Learning basics Formulating Multi-Armed Bandits (MABs) Monte Carlo with example Temporal Difference learning with SARSA and Q Learning Game dev using reinforcment learning and pygame Contextual bandits with codes Training OpenAI gym envs using REINFORCE…

Value-based methods in reinforcement learning explained with code — In my previous posts, I covered many topics on reinforcement learning. Reinforcement Learning basics Formulating Multi-Armed Bandits (MABs) Monte Carlo with example Temporal Difference learning with SARSA and Q Learning Game dev using reinforcment learning and pygame Contextual bandits with codes Training OpenAI gym envs using REINFORCE algorithm

Understanding Genetic evolutionary algorithm with example — Now, this is something I explored while reading about reinforcement learning and appeared to be one of the most fascinating ideas I have come across in recent times. Do you remember the famous concept of ‘Survival of the Fittest’ by Charles Darwin from your bio classes back in school? Somewhat related to it is the idea of ‘Evolution’. Survival of the Fittest describes the mechanism of natural selection by explaining…

Policy gradient methods explained with codes — In my previous parts on reinforcement learning, we have covered many topics ranging from Reinforcement Learning basics Formulating Multi-Armed Bandits (MABs) Monte Carlo with example Temporal Difference learning with SARSA and Q Learning Game dev using reinforcment learning and pygame Contextual bandits with codes The typical target in most…

starting deep reinforcement learning using tensorflow — So, continuing my reinforcement learning blog series which includes Reinforcement Learning basics Formulating Multi-Armed Bandits (MABs) Monte Carlo with example Temporal Difference learning with SARSA and Q Learning Game dev using reinforcment learning and pygame I would be discussing Contextual Bandits and their implementation using tensorflow for a dummy use…