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fix PointMaze (#8)

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* update atari.py

* fix setup.py
pass the pytest

* fix setup.py
pass the pytest

* add args "render"

* change the tensorboard writter

* change the tensorboard writter

* change device, render, tensorboard log location

* change device, render, tensorboard log location

* remove some wrong local files

* fix some tab mistakes and the envs name in continuous/test_xx.py

* add examples and point robot maze environment

* fix some bugs during testing examples

* add dqn network and fix some args

* change back the tensorboard writter's frequency to ensure ppo and a2c can write things normally

* add a warning to collector

* rm some unrelated files

* reformat

* fix a bug in test_dqn due to the model wrong selection

* change atari frame skip and observation to improve performance

* readd some files

* change import

* modified readme

* rm tensorboard log

* update atari and mujoco which are ignored

* rm the wrong lines

* readd the import of PointMaze

* fix a typo in test/discrete/net.py

* add a class declaration to pass the flake8

* fix flake8 errors
This commit is contained in:
Minghao Zhang 2020-03-28 14:36:12 +08:00 committed by GitHub
parent f68f23292e
commit eb7fb37806
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3 changed files with 22 additions and 20 deletions
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3
examples/point_maze_td3.py
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@ -9,13 +9,12 @@ from tianshou.policy import TD3Policy
from tianshou.trainer import offpolicy_trainer
from tianshou.data import Collector, ReplayBuffer
from tianshou.env import VectorEnv, SubprocVectorEnv
from continuous_net import Actor, Critic
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='PointMaze-v0')
parser.add_argument('--task', type=str, default='PointMaze-v1')
parser.add_argument('--seed', type=int, default=1626)
parser.add_argument('--buffer-size', type=int, default=20000)
parser.add_argument('--actor-lr', type=float, default=3e-5)

2
tianshou/env/__init__.py vendored
View File

@ -2,8 +2,10 @@ from tianshou.env.utils import CloudpickleWrapper
from tianshou.env.common import EnvWrapper, FrameStack
from tianshou.env.vecenv import BaseVectorEnv, VectorEnv, \
SubprocVectorEnv, RayVectorEnv
from tianshou.env import mujoco
__all__ = [
'mujoco',
'EnvWrapper',
'FrameStack',
'BaseVectorEnv',

37
tianshou/env/mujoco/point_maze_env.py vendored
View File

@ -233,7 +233,7 @@ class PointMazeEnv(gym.Env):
def valid(row, col):
return self._view.shape[0] > row >= 0 \
and self._view.shape[1] > col >= 0
and self._view.shape[1] > col >= 0
def update_view(x, y, d, row=None, col=None):
if row is None or col is None:
@ -252,36 +252,36 @@ class PointMazeEnv(gym.Env):
if valid(row, col):
self._view[row, col, d] += (
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
if valid(row - 1, col):
self._view[row - 1, col, d] += (
(max(0., 0.5 - row_frac)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
(max(0., 0.5 - row_frac)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
if valid(row + 1, col):
self._view[row + 1, col, d] += (
(max(0., row_frac - 0.5)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
(max(0., row_frac - 0.5)) *
(min(1., col_frac + 0.5) - max(0., col_frac - 0.5)))
if valid(row, col - 1):
self._view[row, col - 1, d] += (
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(max(0., 0.5 - col_frac)))
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(max(0., 0.5 - col_frac)))
if valid(row, col + 1):
self._view[row, col + 1, d] += (
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(max(0., col_frac - 0.5)))
(min(1., row_frac + 0.5) - max(0., row_frac - 0.5)) *
(max(0., col_frac - 0.5)))
if valid(row - 1, col - 1):
self._view[row - 1, col - 1, d] += (
(max(0., 0.5 - row_frac)) * max(0., 0.5 - col_frac))
(max(0., 0.5 - row_frac)) * max(0., 0.5 - col_frac))
if valid(row - 1, col + 1):
self._view[row - 1, col + 1, d] += (
(max(0., 0.5 - row_frac)) * max(0., col_frac - 0.5))
(max(0., 0.5 - row_frac)) * max(0., col_frac - 0.5))
if valid(row + 1, col + 1):
self._view[row + 1, col + 1, d] += (
(max(0., row_frac - 0.5)) * max(0., col_frac - 0.5))
(max(0., row_frac - 0.5)) * max(0., col_frac - 0.5))
if valid(row + 1, col - 1):
self._view[row + 1, col - 1, d] += (
(max(0., row_frac - 0.5)) * max(0., 0.5 - col_frac))
(max(0., row_frac - 0.5)) * max(0., 0.5 - col_frac))
# Draw ant.
robot_x, robot_y = self.wrapped_env.get_body_com("torso")[:2]
@ -376,7 +376,8 @@ class PointMazeEnv(gym.Env):
sensor_readings = np.zeros((self._n_bins, 3))
for ray_idx in range(self._n_bins):
ray_ori = (ori - self._sensor_span * 0.5 + (
2 * ray_idx + 1.0) / (2 * self._n_bins) * self._sensor_span)
2 * ray_idx + 1.0) /
(2 * self._n_bins) * self._sensor_span)
ray_segments = []
# Get all segments that intersect with ray.
for seg in segments:
@ -401,8 +402,8 @@ class PointMazeEnv(gym.Env):
2 if maze_env_utils.can_move(seg_type) else # Block.
None)
if first_seg["distance"] <= self._sensor_range:
sensor_readings[ray_idx][idx] = (
self._sensor_range - first_seg[
sensor_readings[ray_idx][idx] = \
(self._sensor_range - first_seg[
"distance"]) / self._sensor_range
return sensor_readings