The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration.
The Frozen Lake environment is a 4×4 grid which contain four possible areas — Safe (S), Frozen (F), Hole (H) and Goal (G). The agent moves around the grid until it reaches the goal or the hole. If it falls into the hole, it has to start from the beginning and is rewarded the value 0.

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Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.

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Jul 02, 2020 · The agents environment is a frozen lake (as described by the environments name) and this plays a significant role in the agents ability to navigate through the environment. As the surface on which the agent moves is ‘slippery’ full control is taken away from the agent.

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Lake Tsomgo Tsomgo Lake, also known as Tsongmo Lake or Changu Lake, is a glacial lake in the East Sikkim district of the Indian state of Sikkim, some 40 km from the capital Gangtok. Located at an elevation of 3753 m, the lake remains frozen during the winter season.
May 14, 2019 · In this tutorial, we're going to implement a SARSA agent using only Numpy, gym, and Matplotlib. Oh, and if we want to save our model's we'll make use of Pickle as well. SARSA is a straight forward ...

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If you haven't understood anything we have learned so far, don't worry, we will look at all the concepts along with a frozen lake problem. Imagine there is a frozen lake stretching from your home to your office; you have to walk on the frozen lake to reach your office. But oops! There are holes in the frozen lake so you have to be careful while ...

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Nov 06, 2018 · As an example, we tried to create an agent to solve the frozen lake exercise. We implemented the State-Action-Reward-State-Action — or SARSA — algorithm, an RL strategy that learns how to perform a task. [Related Article: Deep Learning with Reinforcement Learning]

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When we last left off, we covered the Q learning algorithm for solving the cart pole problem from the OpenAI Gym. Related to Q learning is the SARSA algorith...

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