Priyam basuinHashtag by IECSERL Part 7 — Applications of Reinforcement LearningTaking a dive into the applications of Reinforcement Learning and looking into some popular case studies.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 6- Introduction to Deep Q-Learning and Deep Q-NetworksExplaining how a deep neural network can be combined with Q-learning.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 5- Implementing an Iterable Q-Table in PythonImplementing the Q-learning method using iteration tables to choose the best action.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 4 — Exploration and ExploitationExplaining how an agent decides whether to extract information from an environment or use old information to change actions.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 3— Optimal Policy and Q-LearningExplaining how an an agent chooses the best policy to get best results.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 2 — Returns, Policy and Value FunctionsExplaining how an agent follows a policy and changes its action based on the reward it gets.May 24, 2020May 24, 2020
Priyam basuinHashtag by IECSERL Part 1 — Introduction to Reinforcement Learning and Markov Decision ProcessA brief introduction to Reinforcement Learning and how we use the concepts of Markov decision processes to explain it.May 24, 2020May 24, 2020