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