def run(args):
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.SafeLoader)
# Create environments.
env = make_pytorch_env(args.env_id)
test_env = make_pytorch_env(
args.env_id, episode_life=False, clip_rewards=False)
# Specify the directory to log.
name = args.config.split('/')[-1].rstrip('.yaml')
time = datetime.now().strftime("%Y%m%d-%H%M")
log_dir = os.path.join(
'logs', args.env_id, f'{name}-seed{args.seed}-{time}')
agent = DMoGQAgent(
env=env, test_env=test_env, log_dir=log_dir,
seed=args.seed, cuda=args.cuda, **config)
agent.run()
# from fqf_iqn_qrdqn.utils import cdf_gauss
# import torch
# print(cdf_gauss(torch.tensor(0.0), torch.tensor(0.0), torch.tensor(1.0)))
import os
import yaml
import argparse
from datetime import datetime
import torch
from fqf_iqn_qrdqn.env import make_pytorch_env
from fqf_iqn_qrdqn.network import DQNBase
from DMoGDiscrete.network import MoGNet
env = make_pytorch_env('PongNoFrameskip-v4')
print(env.action_space.n)
dqn_net = DQNBase(num_channels=env.observation_space.shape[0])
dmog_net = MoGNet(env.action_space.n)
state = env.reset()
state = torch.ByteTensor(state).unsqueeze(0).to('cpu').float() / 255.
dqn_out = dqn_net(state)
# print(dqn_out.shape)
out_pi, out_mu, out_sigma = dmog_net(dqn_out)
print(out_pi)
print(out_mu)
print(out_sigma)
print(out_pi[0, :, 3])
print(torch.sum(out_pi, dim=2, keepdim=True))
print(torch.sum(out_pi, dim=2, keepdim=True).mean())