Created
November 26, 2016 01:39
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import gym | |
import numpy as np | |
import pickle | |
env = gym.make('CartPole-v0') | |
# There are four variable, use a linear model W*x + b so actually five params | |
# Given some param, run a full episode and compute reward | |
def run_episode(W, required_perf=1000, render=False): | |
observation = env.reset() | |
total_reward = 0 | |
N_step = 0 | |
done = False | |
while not done and N_step <= required_perf: | |
if render: | |
env.render() | |
b = np.append(observation, 1.0) | |
action = 1 if np.dot(W, b) > 0 else 0 | |
observation, reward, done, info = env.step(action) | |
total_reward += reward | |
N_step += 1 | |
return total_reward, N_step | |
save_model = 10 | |
def train(train_episode=1000, required_perf=1000): | |
best_W = [] | |
best_reward = 0 | |
for i in range(train_episode): | |
# generate random param | |
W = np.random.rand(5) * 2 - 1 | |
reward, t = run_episode(W) | |
print("Episode {} finished after {} timesteps".format(i, t+1)) | |
if reward > best_reward: | |
best_reward = reward | |
best_W = W | |
if i % save_model == 0: | |
pickle.dump(best_W, open('random_model.p', 'wb')) | |
return best_W, best_reward | |
load_model = False | |
def run(): | |
if load_model: | |
W = pickle.load(open('random_model.p', 'rb')) | |
else: | |
W, best_reward = train(required_perf=1000) | |
print ("Best model reward = {}".format(best_reward)) | |
eval_episode = 105 | |
for i in range(eval_episode): | |
_, t = run_episode(W, render=False) | |
print("Episode {} finished after {} timesteps".format(i, t+1)) | |
env.monitor.start('/tmp/cartpole-experiment-1', force=True) | |
run() | |
env.monitor.close() | |
gym.upload('/tmp/cartpole-experiment-1') |
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