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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Expert paths/trajectories.
For producing or loading expert trajectories in environment.
"""
import tensorflow as tf
import random
import os
import numpy as np
from six.moves import xrange
import pickle
gfile = tf.gfile
def sample_expert_paths(num, env_str, env_spec,
load_trajectories_file=None):
"""Sample a number of expert paths randomly."""
if load_trajectories_file is not None:
if not gfile.Exists(load_trajectories_file):
assert False, 'trajectories file %s does not exist' % load_trajectories_file
with gfile.GFile(load_trajectories_file, 'r') as f:
episodes = pickle.load(f)
episodes = random.sample(episodes, num)
return [ep[1:] for ep in episodes]
return [sample_expert_path(env_str, env_spec)
for _ in xrange(num)]
def sample_expert_path(env_str, env_spec):
"""Algorithmic tasks have known distribution of expert paths we sample from."""
t = random.randint(2, 10) # sequence length
observations = []
actions = [env_spec.initial_act(None)]
rewards = []
if env_str in ['DuplicatedInput-v0', 'Copy-v0']:
chars = 5
random_ints = [int(random.random() * 1000) for _ in xrange(t)]
for tt in xrange(t):
char_idx = tt // 2 if env_str == 'DuplicatedInput-v0' else tt
char = random_ints[char_idx] % chars
observations.append([char])
actions.append([1, (tt + 1) % 2, char])
rewards.append((tt + 1) % 2)
elif env_str in ['RepeatCopy-v0']:
chars = 5
random_ints = [int(random.random() * 1000) for _ in xrange(t)]
for tt in xrange(3 * t + 2):
char_idx = (tt if tt < t else
2 * t - tt if tt <= 2 * t else
tt - 2 * t - 2)
if tt in [t, 2 * t + 1]:
char = chars
else:
char = random_ints[char_idx] % chars
observations.append([char])
actions.append([1 if tt < t else 0 if tt <= 2 * t else 1,
tt not in [t, 2 * t + 1], char])
rewards.append(actions[-1][-2])
elif env_str in ['Reverse-v0']:
chars = 2
random_ints = [int(random.random() * 1000) for _ in xrange(t)]
for tt in xrange(2 * t + 1):
char_idx = tt if tt < t else 2 * t - tt
if tt != t:
char = random_ints[char_idx] % chars
else:
char = chars
observations.append([char])
actions.append([tt < t, tt > t, char])
rewards.append(tt > t)
elif env_str in ['ReversedAddition-v0']:
chars = 3
random_ints = [int(random.random() * 1000) for _ in xrange(1 + 2 * t)]
carry = 0
char_history = []
move_map = {0: 3, 1: 1, 2: 2, 3: 1}
for tt in xrange(2 * t + 1):
char_idx = tt
if tt >= 2 * t:
char = chars
else:
char = random_ints[char_idx] % chars
char_history.append(char)
if tt % 2 == 1:
tot = char_history[-2] + char_history[-1] + carry
carry = tot // chars
tot = tot % chars
elif tt == 2 * t:
tot = carry
else:
tot = 0
observations.append([char])
actions.append([move_map[tt % len(move_map)],
tt % 2 or tt == 2 * t, tot])
rewards.append(tt % 2 or tt == 2 * t)
elif env_str in ['ReversedAddition3-v0']:
chars = 3
random_ints = [int(random.random() * 1000) for _ in xrange(1 + 3 * t)]
carry = 0
char_history = []
move_map = {0: 3, 1: 3, 2: 1, 3: 2, 4:2, 5: 1}
for tt in xrange(3 * t + 1):
char_idx = tt
if tt >= 3 * t:
char = chars
else:
char = random_ints[char_idx] % chars
char_history.append(char)
if tt % 3 == 2:
tot = char_history[-3] + char_history[-2] + char_history[-1] + carry
carry = tot // chars
tot = tot % chars
elif tt == 3 * t:
tot = carry
else:
tot = 0
observations.append([char])
actions.append([move_map[tt % len(move_map)],
tt % 3 == 2 or tt == 3 * t, tot])
rewards.append(tt % 3 == 2 or tt == 3 * t)
else:
assert False, 'No expert trajectories for env %s' % env_str
actions = [
env_spec.convert_env_actions_to_actions(act)
for act in actions]
observations.append([chars])
observations = [np.array(obs) for obs in zip(*observations)]
actions = [np.array(act) for act in zip(*actions)]
rewards = np.array(rewards)
return [observations, actions, rewards, True]
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