Q-Learning Agent playing 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.
• The environment support two different sizes: 4x4 and 8x8grids
• 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.
• 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.

Usage