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from __future__ import print_function
import gym
from gym import wrappers, logger
import numpy as np
from six.moves import cPickle as pickle
import json, sys, os
from os import path
from _policies import BinaryActionLinearPolicy # Different file so it can be unpickled
import argparse
def cem(f, th_mean, batch_size, n_iter, elite_frac, initial_std=1.0):
"""
Generic implementation of the cross-entropy method for maximizing a black-box function
f: a function mapping from vector -> scalar
th_mean: initial mean over input distribution
batch_size: number of samples of theta to evaluate per batch
n_iter: number of batches
elite_frac: each batch, select this fraction of the top-performing samples
initial_std: initial standard deviation over parameter vectors
"""
n_elite = int(np.round(batch_size*elite_frac))
th_std = np.ones_like(th_mean) * initial_std
for _ in range(n_iter):
ths = np.array([th_mean + dth for dth in th_std[None,:]*np.random.randn(batch_size, th_mean.size)])
ys = np.array([f(th) for th in ths])
elite_inds = ys.argsort()[::-1][:n_elite]
elite_ths = ths[elite_inds]
th_mean = elite_ths.mean(axis=0)
th_std = elite_ths.std(axis=0)
yield {'ys' : ys, 'theta_mean' : th_mean, 'y_mean' : ys.mean()}
def do_rollout(agent, env, num_steps, render=False):
total_rew = 0
ob = env.reset()
for t in range(num_steps):
a = agent.act(ob)
(ob, reward, done, _info) = env.step(a)
total_rew += reward
if render and t%3==0: env.render()
if done: break
return total_rew, t+1
if __name__ == '__main__':
logger.set_level(logger.INFO)
parser = argparse.ArgumentParser()
parser.add_argument('--display', action='store_true')
parser.add_argument('target', nargs="?", default="CartPole-v0")
args = parser.parse_args()
env = gym.make(args.target)
env.seed(0)
np.random.seed(0)
params = dict(n_iter=10, batch_size=25, elite_frac = 0.2)
num_steps = 200
# You provide the directory to write to (can be an existing
# directory, but can't contain previous monitor results. You can
# also dump to a tempdir if you'd like: tempfile.mkdtemp().
outdir = '/tmp/cem-agent-results'
env = wrappers.Monitor(env, outdir, force=True)
# Prepare snapshotting
# ----------------------------------------
def writefile(fname, s):
with open(path.join(outdir, fname), 'w') as fh: fh.write(s)
info = {}
info['params'] = params
info['argv'] = sys.argv
info['env_id'] = env.spec.id
# ------------------------------------------
def noisy_evaluation(theta):
agent = BinaryActionLinearPolicy(theta)
rew, T = do_rollout(agent, env, num_steps)
return rew
# Train the agent, and snapshot each stage
for (i, iterdata) in enumerate(
cem(noisy_evaluation, np.zeros(env.observation_space.shape[0]+1), **params)):
print('Iteration %2i. Episode mean reward: %7.3f'%(i, iterdata['y_mean']))
agent = BinaryActionLinearPolicy(iterdata['theta_mean'])
if args.display: do_rollout(agent, env, 200, render=True)
writefile('agent-%.4i.pkl'%i, str(pickle.dumps(agent, -1)))
# Write out the env at the end so we store the parameters of this
# environment.
writefile('info.json', json.dumps(info))
env.close()