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Solution of MountainCar OpenAI Gym problem using Q-Learning.
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""" | |
Q-Learning example using OpenAI gym MountainCar enviornment | |
Author: Moustafa Alzantot (malzantot@ucla.edu) | |
""" | |
import numpy as np | |
import gym | |
from gym import wrappers | |
n_states = 40 | |
iter_max = 10000 | |
initial_lr = 1.0 # Learning rate | |
min_lr = 0.003 | |
gamma = 1.0 | |
t_max = 10000 | |
eps = 0.02 | |
def run_episode(env, policy=None, render=False): | |
obs = env.reset() | |
total_reward = 0 | |
step_idx = 0 | |
for _ in range(t_max): | |
if render: | |
env.render() | |
if policy is None: | |
action = env.action_space.sample() | |
else: | |
a,b = obs_to_state(env, obs) | |
action = policy[a][b] | |
obs, reward, done, _ = env.step(action) | |
total_reward += gamma ** step_idx * reward | |
step_idx += 1 | |
if done: | |
break | |
return total_reward | |
def obs_to_state(env, obs): | |
""" Maps an observation to state """ | |
env_low = env.observation_space.low | |
env_high = env.observation_space.high | |
env_dx = (env_high - env_low) / n_states | |
a = int((obs[0] - env_low[0])/env_dx[0]) | |
b = int((obs[1] - env_low[1])/env_dx[1]) | |
return a, b | |
if __name__ == '__main__': | |
env_name = 'MountainCar-v0' | |
env = gym.make(env_name) | |
env.seed(0) | |
np.random.seed(0) | |
print ('----- using Q Learning -----') | |
q_table = np.zeros((n_states, n_states, 3)) | |
for i in range(iter_max): | |
obs = env.reset() | |
total_reward = 0 | |
## eta: learning rate is decreased at each step | |
eta = max(min_lr, initial_lr * (0.85 ** (i//100))) | |
for j in range(t_max): | |
a, b = obs_to_state(env, obs) | |
if np.random.uniform(0, 1) < eps: | |
action = np.random.choice(env.action_space.n) | |
else: | |
logits = q_table[a][b] | |
logits_exp = np.exp(logits) | |
probs = logits_exp / np.sum(logits_exp) | |
action = np.random.choice(env.action_space.n, p=probs) | |
obs, reward, done, _ = env.step(action) | |
total_reward += (gamma ** j) * reward | |
# update q table | |
a_, b_ = obs_to_state(env, obs) | |
q_table[a][b][action] = q_table[a][b][action] + eta * (reward + gamma * np.max(q_table[a_][b_]) - q_table[a][b][action]) | |
if done: | |
break | |
if i % 100 == 0: | |
print('Iteration #%d -- Total reward = %d.' %(i+1, total_reward)) | |
solution_policy = np.argmax(q_table, axis=2) | |
solution_policy_scores = [run_episode(env, solution_policy, False) for _ in range(100)] | |
print("Average score of solution = ", np.mean(solution_policy_scores)) | |
# Animate it | |
run_episode(env, solution_policy, True) | |
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Line 51 should be:
Also close the environment at line 37: