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Deep Reinforcement Learning Hands-On by Maxim Lapan

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Cross-entropy on FrozenLake

The next environment we'll try to solve using the cross-entropy method is FrozenLake. Its world is from the so-called "grid world" category, when your agent lives in a grid of size 4 × 4 and can move in four directions: up, down, left, and right. The agent always starts at a top-left position, and its goal is to reach the bottom-right cell of the grid. There are holes in the fixed cells of the grid and if you get into those holes, the episode ends and your reward is zero. If the agent reaches the destination cell, then it obtains the reward 1.0 and the episode ends.

To make life more complicated, the world is slippery (it's a frozen lake after all), so the agent's actions do not always turn out as expected: there is ...

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