In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3) algorithms in a variety of challenging environments from the Open AI gym. From there we will progress to teaching an agent to balance the cart pole using Q learning. After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander.
Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020. The reinforcement learning specialization consists of four courses that explore the power of adaptive learning systems and artificial intelligence (AI). On this MOOC course, you will learn how Reinforcement Learning (RL) solutions help to solve real-world problems through trial-and-error interaction by implementing a complete RL solution.
Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications to: (A) offline learning (of stored data) like GAN in (un/semi-/fully-)SL setting such as big data analytics (mining) and visualization; (B) online learning (of real or simulated devices) like DRL in RL setting (with/out environment reward) such as (real or simulated) robotics and control; Our core proposal is adding an (generative/predictive) environment model to the actor-critic (model-free) architecture which results in a model-based actor-critic architecture with temporal-differencing (TD) error and an episodic memory. The proposed AI model is similar to (model-free) DDPG and therefore it's called model-based DDPG. To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents. Our initial limited experiments show that DRL and GAN in model-based actor-critic results in an incremental goal-driven intellignce required to solve each task with similar performance to (model-free) DDPG. Our future focus is to investigate the proposed AI model potential to: (A) unify DRL field inside AI by producing competitive performance compared to the best of model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gap between AI and robotics communities by solving the important problem of reward engineering with learning the reward function by demonstration;
Traditionally, reinforcement learning algorithms were constrained to tiny, discretized grid worlds, which seriously inhibited them from gaining credibility as being viable machine learning tools. Here's a classic example from Richard Sutton's book, which I will be referencing a lot. After Deep Q-Networks  became a hit, people realized that deep learning methods could be used to solve high-dimensional problems. One of the subsequent challenges that the reinforcement learning community faced was figuring out how to deal with continuous action spaces. This is a significant obstacle, since most interesting problems in robotic control, etc., fall into this category.
In this tutorial, I will showcase the upcoming TensorFlow 2.0 features through the lens of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent to solve the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.0, I will do my best to make the DRL aspect approachable as well, including a brief overview of the field. In fact, since the main focus of the 2.0 release is making developers' lives easier, it's a great time to get into DRL with TensorFlow -- our full agent source is under 150 lines! The code is available as a notebook here and online on Google Colab here. As TensorFlow 2.0 is still in an experimental stage, I recommend installing it in a separate (virtual) environment.