Good day and welcome to our course; Reinforcement Learning.
Reinforcement Learning is a really thrilling and necessary area of Machine Learning and AI. Some name it the crown jewel of AI.
On this course, we’ll cowl all of the features associated to Reinforcement Learning or RL. We are going to begin by defining the RL drawback, and evaluate it to the Supervised Learning drawback, and uncover the areas of purposes the place RL can excel. This consists of the issue formulation, ranging from the very fundamentals to the superior utilization of Deep Learning, resulting in the period of Deep Reinforcement Learning.
In our journey, we’ll cowl, as standard, each the theoretical and sensible features, the place we’ll discover ways to implement the RL algorithms and apply them to the well-known issues utilizing libraries like OpenAI Gymnasium, Keras-RL, TensorFlow Brokers or TF-Brokers and Secure Baselines.
The course is split into 6 most important sections:
1- We begin with an introduction to the RL drawback definition, primarily evaluating it to the Supervised studying drawback, and discovering the applying domains and the primary constituents of an RL drawback. We describe right here the well-known OpenAI Gymnasium environments, which will probably be our playground in relation to sensible implementation of the algorithms that we find out about.
2- Within the second half we focus on the primary formulation of an RL drawback as a Markov Choice Course of or MDP, with easy resolution to essentially the most fundamental issues utilizing Dynamic Programming.
3- After being armed with an understanding of MDP, we transfer on to discover the answer house of the MDP drawback, and what the completely different options past DP, which incorporates model-based and model-free options. We are going to focus on this half on model-free options, and defer model-based options to the final half. On this half, we describe the Monte-Carlo and Temporal-Distinction sampling based mostly strategies, together with the well-known and necessary Q-learning algorithm, and SARSA. We are going to describe the sensible utilization and implementation of Q-learning and SARSA on management tabular maze issues from OpenAI Gymnasium environments.
4- To maneuver past easy tabular issues, we might want to find out about operate approximation in RL, which ends up in the mainstream RL strategies at this time utilizing Deep Learning, or Deep Reinforcement Learning (DRL). We are going to describe right here the breakthrough algorithm of DeepMind that solved the Atari video games and AlphaGO, which is Deep Q-Networks or DQN. We additionally focus on how we are able to remedy Atari video games issues utilizing DQN in apply utilizing Keras-RL and TF-Brokers.
5- Within the fifth half, we transfer to Superior DRL algorithms, primarily below a household known as Coverage based mostly strategies. We focus on right here Coverage Gradients, DDPG, Actor-Critic, A2C, A3C, TRPO and PPO strategies. We additionally focus on the necessary Secure Baseline library to implement all these algorithms on completely different environments in OpenAI Gymnasium, like Atari and others.
6- Lastly, we discover the model-based household of RL strategies, and importantly, differentiating model-based RL from planning, and exploring the entire spectrum of RL strategies.
Hopefully, you get pleasure from this course, and discover it helpful.
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