def main():
"""
in lunar_lander.py, add: (line 280 before self.world.Step(1.0/FPS, 6*30, 2*30))
self.lander.ApplyForceToCenter((
np.random.normal(loc=SIDE_ENGINE_POWER, scale=SIDE_ENGINE_POWER / 3), # side
np.random.normal(loc=MAIN_ENGINE_POWER, scale=MAIN_ENGINE_POWER / 3) # main
#np.random.normal(loc=SIDE_ENGINE_POWER / 6, scale=SIDE_ENGINE_POWER / 3), #side
#np.random.normal(loc=MAIN_ENGINE_POWER / 6, scale=MAIN_ENGINE_POWER / 3) #main
#self.np_random.uniform(-INITIAL_RANDOM/32, INITIAL_RANDOM/32),
#self.np_random.uniform(-INITIAL_RANDOM/32, INITIAL_RANDOM/32)
), True)
"""
num_iter = 1000
with open('results/sarsa_data/sarsa_Q_5X4Ys.json') as json_file:
Q = json.load(json_file)
env = lander.LunarLander()
r_seq = noisy_lander(env, Q, render=False, num_iter=num_iter, seg=10)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Sarsa Agent reward (Force)')
plt.savefig("results/sarsa_Force_agent_slightbb.png")
np.savetxt("results/sarsa_Force_agent_slightbb.txt", y)
def main():
num_iter = 100000
env = lander.LunarLander()
Q, r_seq = sarsa_lander(env, render=True, num_iter=num_iter, seg=50)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Sarsa reward')
plt.savefig("sarsa_reward.png")
def main():
num_iter = 10000
env = lander.LunarLander()
Q, r_seq = sarsa_lander(env, render=True, num_iter=num_iter, seg=100)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Noisy Sarsa reward')
plt.savefig("results/noisy_sarsa_reward.png")
np.savetxt("results/noisy_sarsa_reward.txt", y)
def main():
num_iter = 10000
env = lander.LunarLander()
r_seq = random_lander(env, render=False, num_iter=num_iter, seg=100)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Random reward')
plt.savefig("results/random_reward.png")
np.savetxt("results/random_reward.txt", y)
def main():
lr = 1e-2
theta = np.random.randn(4, 9) / 100.0
env = lander.LunarLander()
theta = linear_approximation_lander(theta,
lr,
env,
render=True,
num_iter=100)
print("Final theta: ", theta)
np.savetxt("weights/linear_approximation_theta.txt", theta)
def main():
num_iter = 1000
with open('results/sarsa_data/sarsa_Q_3XY.json') as json_file:
Q = json.load(json_file)
env = lander.LunarLander()
r_seq = noisy_lander(env, Q, render=False, num_iter=num_iter, seg=10)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Noisy Agent reward')
plt.savefig("results/noisy_agent.png")
np.savetxt("results/noisy_agent.txt", y)
def main():
num_iter = 10000
env = lander.LunarLander()
Q, r_seq = sarsa_lander(env, render=True, num_iter=num_iter, seg=100)
y = np.array(r_seq)
x = np.linspace(0, num_iter, y.shape[0])
plt.plot(x, y, label='Sarsa reward')
plt.savefig("results/sarsa_reward.png")
np.savetxt("results/sarsa_reward.txt", y)
q = json.dumps(Q, indent=4)
f = open("results/sarsa_Q.json", "w")
f.write(q)
f.close()
finishes = np.array(finishes)
states = np.squeeze(states)
next_states = np.squeeze(next_states)
q_vals_next_state = model.predict_on_batch(next_states)
q_vals_target = model.predict_on_batch(states)
max_q_values_next_state = np.amax(q_vals_next_state, axis=1)
q_vals_target[np.arange(batch_size), actions] = rewards + gamma * (
max_q_values_next_state) * (1 - finishes)
model.fit(states, q_vals_target, verbose=0)
global epsilon
if epsilon > min_eps:
epsilon *= 0.996
if __name__ == '__main__':
env = lander.LunarLander()
# env.seed(0)
num_episodes = 400
np.random.seed(0)
scores = []
for i in range(num_episodes + 1):
score = 0
state = env.reset()
finished = False
if i != 0 and i % 50 == 0:
model.save(".\saved_models\model_" + str(i) + "_episodes.h5")
for j in range(3000):
state = np.reshape(state, (1, 8))
if np.random.random() <= epsilon:
action = np.random.choice(4)
else:
def main():
baseline_lander(lander.LunarLander(), render=True)
def main():
random_lander(lander.LunarLander(), render=True)
def main():
heuristic_lander(lander.LunarLander(), render=True)
