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simple_agent.py
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# Copyright (c) 2018 PaddlePaddle 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.
import numpy as np
import parl
from parl import layers
from paddle import fluid
from parl.utils import ReplayMemory
class MAAgent(parl.Agent):
def __init__(self,
algorithm,
agent_index=None,
obs_dim_n=None,
act_dim_n=None,
batch_size=None,
speedup=False):
assert isinstance(agent_index, int)
assert isinstance(obs_dim_n, list)
assert isinstance(act_dim_n, list)
assert isinstance(batch_size, int)
assert isinstance(speedup, bool)
self.agent_index = agent_index
self.obs_dim_n = obs_dim_n
self.act_dim_n = act_dim_n
self.batch_size = batch_size
self.speedup = speedup
self.n = len(act_dim_n)
self.memory_size = int(1e6)
self.min_memory_size = batch_size * 25 # batch_size * args.max_episode_len
self.rpm = ReplayMemory(
max_size=self.memory_size,
obs_dim=self.obs_dim_n[agent_index],
act_dim=self.act_dim_n[agent_index])
self.global_train_step = 0
super(MAAgent, self).__init__(algorithm)
# Attention: In the beginning, sync target model totally.
self.alg.sync_target(decay=0)
def build_program(self):
self.pred_program = fluid.Program()
self.learn_program = fluid.Program()
self.next_q_program = fluid.Program()
self.next_a_program = fluid.Program()
with fluid.program_guard(self.pred_program):
obs = layers.data(
name='obs',
shape=[self.obs_dim_n[self.agent_index]],
dtype='float32')
self.pred_act = self.alg.predict(obs)
with fluid.program_guard(self.learn_program):
obs_n = [
layers.data(
name='obs' + str(i),
shape=[self.obs_dim_n[i]],
dtype='float32') for i in range(self.n)
]
act_n = [
layers.data(
name='act' + str(i),
shape=[self.act_dim_n[i]],
dtype='float32') for i in range(self.n)
]
target_q = layers.data(name='target_q', shape=[], dtype='float32')
self.critic_cost = self.alg.learn(obs_n, act_n, target_q)
with fluid.program_guard(self.next_q_program):
obs_n = [
layers.data(
name='obs' + str(i),
shape=[self.obs_dim_n[i]],
dtype='float32') for i in range(self.n)
]
act_n = [
layers.data(
name='act' + str(i),
shape=[self.act_dim_n[i]],
dtype='float32') for i in range(self.n)
]
self.next_Q = self.alg.Q_next(obs_n, act_n)
with fluid.program_guard(self.next_a_program):
obs = layers.data(
name='obs',
shape=[self.obs_dim_n[self.agent_index]],
dtype='float32')
self.next_action = self.alg.predict_next(obs)
if self.speedup:
self.pred_program = parl.compile(self.pred_program)
self.learn_program = parl.compile(self.learn_program,
self.critic_cost)
self.next_q_program = parl.compile(self.next_q_program)
self.next_a_program = parl.compile(self.next_a_program)
def predict(self, obs):
obs = np.expand_dims(obs, axis=0)
obs = obs.astype('float32')
act = self.fluid_executor.run(
self.pred_program, feed={'obs': obs},
fetch_list=[self.pred_act])[0]
return act[0]
def learn(self, agents):
self.global_train_step += 1
# only update parameter every 100 steps
if self.global_train_step % 100 != 0:
return 0.0
if self.rpm.size() <= self.min_memory_size:
return 0.0
batch_obs_n = []
batch_act_n = []
batch_obs_new_n = []
rpm_sample_index = self.rpm.make_index(self.batch_size)
for i in range(self.n):
batch_obs, batch_act, _, batch_obs_new, _ \
= agents[i].rpm.sample_batch_by_index(rpm_sample_index)
batch_obs_n.append(batch_obs)
batch_act_n.append(batch_act)
batch_obs_new_n.append(batch_obs_new)
_, _, batch_rew, _, batch_isOver \
= self.rpm.sample_batch_by_index(rpm_sample_index)
# compute target q
target_q = 0.0
target_act_next_n = []
for i in range(self.n):
feed = {'obs': batch_obs_new_n[i]}
target_act_next = agents[i].fluid_executor.run(
agents[i].next_a_program,
feed=feed,
fetch_list=[agents[i].next_action])[0]
target_act_next_n.append(target_act_next)
feed_obs = {'obs' + str(i): batch_obs_new_n[i] for i in range(self.n)}
feed_act = {
'act' + str(i): target_act_next_n[i]
for i in range(self.n)
}
feed = feed_obs.copy()
feed.update(feed_act) # merge two dict
target_q_next = self.fluid_executor.run(
self.next_q_program, feed=feed, fetch_list=[self.next_Q])[0]
target_q += (
batch_rew + self.alg.gamma * (1.0 - batch_isOver) * target_q_next)
feed_obs = {'obs' + str(i): batch_obs_n[i] for i in range(self.n)}
feed_act = {'act' + str(i): batch_act_n[i] for i in range(self.n)}
target_q = target_q.astype('float32')
feed = feed_obs.copy()
feed.update(feed_act)
feed['target_q'] = target_q
critic_cost = self.fluid_executor.run(
self.learn_program, feed=feed, fetch_list=[self.critic_cost])[0]
self.alg.sync_target()
return critic_cost
def add_experience(self, obs, act, reward, next_obs, terminal):
self.rpm.append(obs, act, reward, next_obs, terminal)