add test.py

Author: jonzamora

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

@@ -100,6 +100,13 @@ intro-ai-series
     You can write something up or use the marketing description.
 -->
 
+## 3.1 Environment Setup
+
+```
+conda env create -f environment.yaml
+conda activate ai
+```
+
 Workshop "Intro to AI: Search Algorithms" consists of <!-- however many you choose --> components:
 - [Notebook](<!-- Local Path to Notebook -->) with completed code and explanations.
 - [Summary Graphic](<!-- Local Path to Summary Graphic -->) to summarize key points of the workshop.

FA22/intro-ai-series/workshop-1-ai-search-algorithms/environment.yaml

@@ -0,0 +1,11 @@
+name: ai
+channels:
+  - defaults
+dependencies:
+  - python=3.8
+  - pip
+  - pip:
+    - termcolor
+    - gym
+    - gym[box2d]
+    - moviepy

FA22/intro-ai-series/workshop-1-ai-search-algorithms/src/test.py

@@ -0,0 +1,79 @@
+import gym
+from gym.utils.save_video import save_video
+from time import sleep
+from search import *
+
+'''
+Key:
+S = Start
+F = Frozen
+H = Hole
+G = Goal
+'''
+
+MAPS = {"4x4":["SFFF", "FHFH", "FFFH", "HFFG"],
+        "8x8":["SFFFFFFF", "FFFFFFFF", "FFFHFFFF", "FFFFFHFF", 
+               "FFFHFFFF", "FHHFFFHF", "FHFFHFHF", "FFFHFFFG"]}
+MAP = "8x8" # can be 8x8 or 4x4
+
+USE_DETERMINISTIC=True # runs a deterministic path which results in Success
+TRAJECTORY = {"4x4": [1, 1, 2, 2, 1, 2],
+              "8x8": [2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1]}
+
+ACTIONS = {0: "LEFT", 1: "DOWN", 2: "RIGHT", 3: "UP"}
+RENDER_MODE="rgb_array_list"
+
+
+def main():
+    env = gym.make("FrozenLake-v1", desc=MAPS[MAP], render_mode=RENDER_MODE, is_slippery=False)
+    env.action_space.seed(42)
+    observation, info = env.reset(seed=42)
+
+    step_starting_index = 0
+    episode_index = 0
+
+    if USE_DETERMINISTIC:
+        for step, action in enumerate(TRAJECTORY[MAP]):
+            observation, reward, terminated, truncated, info = env.step(action) # deterministic action
+
+            print("REWARD:", reward, "| ACTION:", ACTIONS[action])
+            sleep(0.01) # for spacing out rendering of frames
+
+            if terminated or truncated:
+                save_video(
+                    env.render(),
+                    "videos",
+                    fps=env.metadata["render_fps"],
+                    step_starting_index=step_starting_index,
+                    episode_index=episode_index,
+                    name_prefix=MAP
+                )
+                step_starting_index = step + 1
+                episode_index += 1
+                observation, info = env.reset()
+        env.close()
+
+    else:
+        for step in range(1000):
+            action = env.action_space.sample() # randomly sampled action
+            observation, reward, terminated, truncated, info = env.step(action)
+
+            print("REWARD:", reward, "| ACTION:", ACTIONS[action])
+            sleep(0.01) # for spacing out rendering of frames
+
+            if terminated or truncated:
+                save_video(
+                    env.render(),
+                    "videos",
+                    fps=env.metadata["render_fps"],
+                    step_starting_index=step_starting_index,
+                    episode_index=episode_index,
+                    name_prefix=MAP
+                )
+                step_starting_index = step + 1
+                episode_index += 1
+                observation, info = env.reset()
+        env.close()
+
+if __name__ == "__main__":
+    main()