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import gym
from dqn import Agent
from utils import plotLearning
import numpy as np
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
agent = Agent(gamma=0.99, epsilon=1.0, batch_size=64, n_actions=4, eps_end=0.01,
input_dims=[8], lr=0.001)
scores, eps_history = [], []
n_games = 500
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.store_transition(observation, action, reward,
observation_, done)
agent.learn()
observation = observation_
scores.append(score)
eps_history.append(agent.epsilon)
avg_score = np.mean(scores[-100:])
print('episode ', i, 'score %.2f' % score,
'average score %.2f' % avg_score,
'epsilon %.2f' % agent.epsilon)
x = [i + 1 for i in range(n_games)]
filename = 'lunar_lander.png'
plotLearning(x, scores, eps_history, filename)
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