initial commit

main.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+from Environment import Env
+from DynaQ import DynaQ
+
+import matplotlib.pyplot as plt
+
+
+def main():
+    # Parameters
+    alpha = 0.1
+    gamma = 0.9
+    epsilon = 1
+    epsilon_decay = 0.05
+    epsilon_min = 0.01
+    episodes = 100
+    start = 0
+    goal = 10
+    runs = 10
+
+    # Result arrays
+    results0 = np.zeros(episodes)
+    results10 = np.zeros(episodes)
+    results50 = np.zeros(episodes)
+
+    # Build environment
+    env = Env(start=start, goal=goal)
+
+    # Learn for agents with planning steps 0, 10, 50
+    # Do 10 runs for each agent and take the average of the results
+    for run in range(runs):
+        agent0 = DynaQ(env=env, alpha=alpha, gamma=gamma, epsilon=epsilon, n_steps=0, episodes=episodes)
+        agent0.learn(epsilon_min=epsilon_min, epsilon_decay=epsilon_decay, run=run)
+        results0 += np.array(agent0.steps_per_episode)
+        agent10 = DynaQ(env=env, alpha=alpha, gamma=gamma, epsilon=epsilon, n_steps=10, episodes=episodes)
+        agent10.learn(epsilon_min=epsilon_min, epsilon_decay=epsilon_decay, run=run)
+        results10 += np.array(agent10.steps_per_episode)
+        agent50 = DynaQ(env=env, alpha=alpha, gamma=gamma, epsilon=epsilon, n_steps=50, episodes=episodes)
+        agent50.learn(epsilon_min=epsilon_min, epsilon_decay=epsilon_decay, run=run)
+        results50 += np.array(agent50.steps_per_episode)
+
+    results0 = results0 / runs
+    results10 = results10 / runs
+    results50 = results50 / runs
+
+    # Plot the results
+    plt.figure()
+    plt.plot(range(episodes), results0.tolist(), label='0 planning steps')
+    plt.plot(range(episodes), results10.tolist(), label='10 planning steps')
+    plt.plot(range(episodes), results50.tolist(), label='50 planning steps')
+    plt.legend()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()