Programming an agent using an OpenAI Gym environment

The environment considered for this section is the Frozen Lake v0. The actual documentation of the concerned environment can be found at https://gym.openai.com/envs/FrozenLake-v0/.

This environment consists of 4 x 4 grids representing a lake. Thus, we have 16 grid blocks, where each block can be a start block(S), frozen block(F), goal block(G), or a hole block(H). Thus, the objective of the agent is to learn to navigate from start to goal without falling in the hole:

import Gym
env = Gym.make('FrozenLake-v0')    #loads the environment FrozenLake-v0
env.render()                       # will output the environment and position of the agent

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SFFF
FHFH
FFFH
HFFG

At any given state, an agent has four actions to perform, which are up, down, left, and right. The reward at each step is 0 except the one leading to the goal state, then the reward would be 1. We start from the S state and our goal is to reach the G state without landing up in the H state in the most optimized path through the F states.