Update README.md

README.md

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   # **Q-Learning** Agent playing1 **FrozenLake-v2**
   This is a trained model of a **Q-Learning** agent playing **FrozenLake-v2**. The **goal** of the agent is to navigate from starting state to the goal state by walking only on Frozen tiles and avoid holes.
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   The environment support two different sizes: `4x4` and `8x8`grids
   The environment has two modes: 
     - `is_slippery=True` agent may not move in the desired direction due to the slippery nature of the tails.
     - `is_slippery=False` agent moves in the desired direction due to the non-slippery nature of the tails.
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   How to create the FrozenLake environment?
     - ```gym.make("FrozenLake-v1", desc=None, map_name="4x4", is_slippery=True, render_mode="rgb_array")```
     - `desc=None` specify non-predetermined maps, `map_name` will be used. You can also specify custom map like ```desc = ["SFFH", "HFHF", "FFHF", "HFFG"]```. If both are None, random `8x8´ map with `80%` of locations frozen will be generated.