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categorical_dqn.py
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categorical_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.helper import clip_by_value
from common.network import nature_head
from common.env import AtariWrapper
from dqn import DQN, update
def q_function(obs, num_actions, min_v, max_v, num_bins, scope):
with nn.parameter_scope(scope):
out = nature_head(obs)
out = PF.affine(out, num_actions * num_bins, name='output')
out = F.reshape(out, (-1, num_actions, num_bins))
probs = F.exp(out) / F.sum(F.exp(out), axis=2, keepdims=True)
dists = F.arange(0, num_bins) * (max_v - min_v) / (num_bins - 1) + min_v
values = F.sum(probs * F.reshape(dists, (1, 1, num_bins)), axis=2)
return values, probs, F.reshape(dists, (-1, 1))
class CategoricalDQN(DQN):
def __init__(self,
q_function,
num_actions,
min_v,
max_v,
num_bins,
batch_size,
gamma,
lr):
self.min_v = min_v
self.max_v = max_v
self.num_bins = num_bins
super().__init__(q_function, num_actions, batch_size, gamma, lr)
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.infer_probs_t, _ = self.q_function(infer_obs_t, self.num_actions,
self.min_v, self.max_v,
self.num_bins, 'q_func')
self.infer_t = F.sink(self.infer_q_t, self.infer_probs_t)
# 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, probs_t, dists = self.q_function(self.obss_t, self.num_actions,
self.min_v, self.max_v,
self.num_bins, 'q_func')
q_tp1, probs_tp1, _ = self.q_function(self.obss_tp1, self.num_actions,
self.min_v, self.max_v,
self.num_bins, 'target_q_func')
expand_last = lambda x: F.reshape(x, x.shape + (1,))
flat = lambda x: F.reshape(x, (-1, 1))
# extract selected dimension
a_t_one_hot = expand_last(F.one_hot(self.acts_t, (self.num_actions,)))
probs_t_selected = F.max(probs_t * a_t_one_hot, axis=1)
# extract max dimension
_, indices = F.max(q_tp1, axis=1, keepdims=True, with_index=True)
a_tp1_one_hot = expand_last(F.one_hot(indices, (self.num_actions,)))
probs_tp1_best = F.max(probs_tp1 * a_tp1_one_hot, axis=1)
# clipping reward
clipped_rews_tp1 = clip_by_value(self.rews_tp1, -1.0, 1.0)
disc_q_tp1 = F.reshape(dists, (1, -1)) * (1.0 - self.ters_tp1)
t_z = clip_by_value(clipped_rews_tp1 + self.gamma * disc_q_tp1,
self.min_v, self.max_v)
# update indices
b = (t_z - self.min_v) / ((self.max_v - self.min_v) / (self.num_bins - 1))
l = F.floor(b)
l_mask = F.reshape(F.one_hot(flat(l), (self.num_bins,)),
(-1, self.num_bins, self.num_bins))
u = F.ceil(b)
u_mask = F.reshape(F.one_hot(flat(u), (self.num_bins,)),
(-1, self.num_bins, self.num_bins))
m_l = expand_last(probs_tp1_best * (1 - (b - l)))
m_u = expand_last(probs_tp1_best * (b - l))
m = F.sum(m_l * l_mask + m_u * u_mask, axis=1)
m.need_grad = False
self.loss = -F.mean(F.sum(m * F.log(probs_t_selected + 1e-10), axis=1))
# 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 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 = CategoricalDQN(q_function, num_actions, args.min_v, args.max_v,
args.num_bins, 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('--seed', type=int, default=0)
parser.add_argument('--batch-size', type=int, default=32)
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('--final-step', type=int, default=10 ** 7)
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('--min-v', type=float, default=-10.0)
parser.add_argument('--max-v', type=float, default=10.0)
parser.add_argument('--num-bins', type=int, default=51)
parser.add_argument('--logdir', type=str, default='categorical_dqn')
parser.add_argument('--load', type=str)
parser.add_argument('--device', type=int, default='0')
parser.add_argument('--gpu', action='store_true')
parser.add_argument('--render', action='store_true')
args = parser.parse_args()
main(args)