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dqn.py
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import numpy as np
import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.functions as F
import nnabla.solvers as S
import argparse
import gym
from nnabla.ext_utils import get_extension_context
from common.buffer import ReplayBuffer
from common.log import prepare_monitor
from common.experiment import evaluate, train
from common.exploration import LinearlyDecayEpsilonGreedy
from common.env import AtariWrapper
from common.helper import clip_by_value
from common.network import nature_head
def pixel_to_float(obs):
return np.array(obs, dtype=np.float32) / 255.0
def q_function(obs, num_actions, scope):
with nn.parameter_scope(scope):
out = nature_head(obs)
return PF.affine(out, num_actions, name='output')
class DQN:
def __init__(self, q_function, num_actions, batch_size, gamma, lr):
self.q_function = q_function
self.num_actions = num_actions
self.batch_size = batch_size
self.gamma = gamma
self.lr = lr
self._build()
def _build(self):
# infer variable
self.infer_obs_t = infer_obs_t = nn.Variable((1, 4, 84, 84))
# inference output
self.infer_q_t = self.q_function(infer_obs_t, self.num_actions,
scope='q_func')
# train variables
self.obss_t = nn.Variable((self.batch_size, 4, 84, 84))
self.acts_t = nn.Variable((self.batch_size, 1))
self.rews_tp1 = nn.Variable((self.batch_size, 1))
self.obss_tp1 = nn.Variable((self.batch_size, 4, 84, 84))
self.ters_tp1 = nn.Variable((self.batch_size, 1))
# training output
q_t = self.q_function(self.obss_t, self.num_actions, scope='q_func')
q_tp1 = self.q_function(self.obss_tp1, self.num_actions,
scope='target_q_func')
# select one dimension
a_one_hot = F.one_hot(self.acts_t, (self.num_actions,))
q_t_selected = F.sum(q_t * a_one_hot, axis=1, keepdims=True)
q_tp1_best = F.max(q_tp1, axis=1, keepdims=True)
# reward clipping
clipped_rews_tp1 = clip_by_value(self.rews_tp1, -1.0, 1.0)
# loss calculation
y = clipped_rews_tp1 + self.gamma * q_tp1_best * (1.0 - self.ters_tp1)
self.loss = F.mean(F.huber_loss(q_t_selected, y))
# optimizer
self.solver = S.RMSprop(self.lr, 0.95, 1e-2)
# weights and biases
with nn.parameter_scope('q_func'):
self.params = nn.get_parameters()
with nn.parameter_scope('target_q_func'):
self.target_params = nn.get_parameters()
# set q function parameters to solver
self.solver.set_parameters(self.params)
def infer(self, obs_t):
self.infer_obs_t.d = np.array(pixel_to_float([obs_t]))
self.infer_q_t.forward(clear_buffer=True)
return np.argmax(self.infer_q_t.d[0])
def evaluate(self, obs_t):
if np.random.random() < 0.05:
return np.random.randint(self.num_actions)
return self.infer(obs_t)
def train(self, obss_t, acts_t, rews_tp1, obss_tp1, ters_tp1):
self.obss_t.d = np.array(obss_t)
self.acts_t.d = np.array(acts_t)
self.rews_tp1.d = np.array(rews_tp1)
self.obss_tp1.d = np.array(obss_tp1)
self.ters_tp1.d = np.array(ters_tp1)
self.loss.forward()
self.solver.zero_grad()
self.loss.backward(clear_buffer=True)
self.solver.clip_grad_by_norm(10.0)
self.solver.update()
return self.loss.d
def update_target(self):
for key in self.target_params.keys():
self.target_params[key].data.copy_from(self.params[key].data)
def reset(self, step):
pass
def update(model, buffer, target_update_inteval):
def _func(step):
experiences = buffer.sample()
obss_t = []
acts_t = []
rews_tp1 = []
obss_tp1 = []
ters_tp1 = []
for experience in experiences:
obss_t.append(experience['obs_t'])
acts_t.append(experience['act_t'])
rews_tp1.append(experience['rew_tp1'])
obss_tp1.append(experience['obs_tp1'])
ters_tp1.append(experience['ter_tp1'])
loss = model.train(pixel_to_float(obss_t), acts_t, rews_tp1,
pixel_to_float(obss_tp1), ters_tp1)
if step % target_update_inteval == 0:
model.update_target()
return [loss]
return _func
def main(args):
if args.gpu:
ctx = get_extension_context('cudnn', device_id=str(args.device))
nn.set_default_context(ctx)
# atari environment
env = AtariWrapper(gym.make(args.env), args.seed, episodic=True)
eval_env = AtariWrapper(gym.make(args.env), 50, episodic=False)
num_actions = env.action_space.n
# action-value function built with neural network
model = DQN(q_function, num_actions, args.batch_size, args.gamma, args.lr)
if args.load is not None:
nn.load_parameters(args.load)
model.update_target()
buffer = ReplayBuffer(args.buffer_size, args.batch_size)
exploration = LinearlyDecayEpsilonGreedy(num_actions, args.epsilon, 0.1,
args.schedule_duration)
monitor = prepare_monitor(args.logdir)
update_fn = update(model, buffer, args.target_update_interval)
eval_fn = evaluate(eval_env, model, render=args.render)
train(env, model, buffer, exploration, monitor, update_fn, eval_fn,
args.final_step, args.update_start, args.update_interval,
args.save_interval, args.evaluate_interval, ['loss'])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--lr', type=float, default=2.5e-4)
parser.add_argument('--buffer-size', type=int, default=10 ** 5)
parser.add_argument('--epsilon', type=float, default=1.0)
parser.add_argument('--schedule-duration', type=int, default=10 ** 6)
parser.add_argument('--target-update-interval', type=int, default=10 ** 4)
parser.add_argument('--update-start', type=int, default=5 * 10 ** 4)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--evaluate-interval', type=int, default=10 ** 6)
parser.add_argument('--save-interval', type=int, default=10 ** 6)
parser.add_argument('--final-step', type=int, default=10 ** 7)
parser.add_argument('--logdir', type=str, default='dqn')
parser.add_argument('--load', type=str)
parser.add_argument('--device', type=str, default='0')
parser.add_argument('--gpu', action='store_true')
parser.add_argument('--render', action='store_true')
args = parser.parse_args()
main(args)