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Commit cd75fdb

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add test.py
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‎FA22/intro-ai-series/workshop-1-ai-search-algorithms/README.md

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You can write something up or use the marketing description.
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-->
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## 3.1 Environment Setup
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```
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conda env create -f environment.yaml
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conda activate ai
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```
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Workshop "Intro to AI: Search Algorithms" consists of <!-- however many you choose --> components:
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- [Notebook](<!-- Local Path to Notebook -->) with completed code and explanations.
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- [Summary Graphic](<!-- Local Path to Summary Graphic -->) to summarize key points of the workshop.
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name: ai
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channels:
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- defaults
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dependencies:
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- python=3.8
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- pip
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- pip:
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- termcolor
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- gym
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- gym[box2d]
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- moviepy
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import gym
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from gym.utils.save_video import save_video
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from time import sleep
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from search import *
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'''
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Key:
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S = Start
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F = Frozen
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H = Hole
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G = Goal
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'''
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MAPS = {"4x4":["SFFF", "FHFH", "FFFH", "HFFG"],
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"8x8":["SFFFFFFF", "FFFFFFFF", "FFFHFFFF", "FFFFFHFF",
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"FFFHFFFF", "FHHFFFHF", "FHFFHFHF", "FFFHFFFG"]}
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MAP = "8x8" # can be 8x8 or 4x4
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USE_DETERMINISTIC=True # runs a deterministic path which results in Success
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TRAJECTORY = {"4x4": [1, 1, 2, 2, 1, 2],
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"8x8": [2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1]}
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ACTIONS = {0: "LEFT", 1: "DOWN", 2: "RIGHT", 3: "UP"}
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RENDER_MODE="rgb_array_list"
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def main():
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env = gym.make("FrozenLake-v1", desc=MAPS[MAP], render_mode=RENDER_MODE, is_slippery=False)
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env.action_space.seed(42)
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observation, info = env.reset(seed=42)
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step_starting_index = 0
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episode_index = 0
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if USE_DETERMINISTIC:
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for step, action in enumerate(TRAJECTORY[MAP]):
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observation, reward, terminated, truncated, info = env.step(action) # deterministic action
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print("REWARD:", reward, "| ACTION:", ACTIONS[action])
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sleep(0.01) # for spacing out rendering of frames
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if terminated or truncated:
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save_video(
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env.render(),
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"videos",
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fps=env.metadata["render_fps"],
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step_starting_index=step_starting_index,
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episode_index=episode_index,
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name_prefix=MAP
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)
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step_starting_index = step + 1
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episode_index += 1
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observation, info = env.reset()
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env.close()
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else:
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for step in range(1000):
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action = env.action_space.sample() # randomly sampled action
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observation, reward, terminated, truncated, info = env.step(action)
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print("REWARD:", reward, "| ACTION:", ACTIONS[action])
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sleep(0.01) # for spacing out rendering of frames
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if terminated or truncated:
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save_video(
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env.render(),
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"videos",
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fps=env.metadata["render_fps"],
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step_starting_index=step_starting_index,
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episode_index=episode_index,
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name_prefix=MAP
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)
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step_starting_index = step + 1
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episode_index += 1
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observation, info = env.reset()
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env.close()
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if __name__ == "__main__":
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main()
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