Hello World, today (August 25, 2020). I trained an agent to play a navigate successfully a game called frozen lake The game can be found at https://gym.openai.com/ DISCOVERIES: learing rate: setting learning rate at 0.1 the qtable change by very little and the accumulated reward was also low setting learning rate at 0.7 increasing accumulated reward significantly , but the qtable changed rapidly setting learning rate at 0.5 the agent achieve a good amount of accumulated reward and the q table changed rapidly number of episodes: increasing the number total number of episode generally increased the accumulated reward and change in the qtable was significant. reinforcement learning: q-learning using environments in the gym package https://gym.openai.com/ implementing epsilon greedy algorithm with exponential decay I employ you to implement the https://github.com/ibkvictor/frozen-lake-agent/blob/master/frozen_lake.ipynb and learn something cool. please encourage me by forking this repo and giving it a star. Your friend Victor.