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Exercise

Solving 8x8 Frozen Lake with Q-learning

In this exercise, you'll apply the Q-learning algorithm to learn an optimal policy for navigating through the 8x8 Frozen Lake environment, this time with the "slippery" condition enabled. The challenge introduces stochastic transitions, making the agent's movement unpredictable and thus more closely simulating real-world scenarios.

A Q-table Q has been initialized and pre-loaded for you, along with the update_q_table() function from the previous exercise and an empty list rewards_per_episode that will contain the total reward accumulated through each episode.

Instructions

100 XP
  • For each episode, execute the selected action and observe the reward and next state.
  • Update the Q-table.
  • Append the total_reward to the rewards_per_episode list.