# Copyright 2018 Tensorforce Team. 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.
# ==============================================================================
from collections import OrderedDict
import numpy as np
from tensorforce import TensorforceError
from tensorforce.environments import Environment
[docs]class PyGameLearningEnvironment(Environment):
"""
[PyGame Learning Environment](https://github.com/ntasfi/PyGame-Learning-Environment/)
environment adapter (specification key: `ple`, `pygame_learning_environment`).
May require:
```bash
sudo apt-get install git python3-dev python3-setuptools python3-numpy python3-opengl \\
libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev libsmpeg-dev libsdl1.2-dev \\
libportmidi-dev libswscale-dev libavformat-dev libavcodec-dev libtiff5-dev libx11-6 \\
libx11-dev fluid-soundfont-gm timgm6mb-soundfont xfonts-base xfonts-100dpi xfonts-75dpi \\
xfonts-cyrillic fontconfig fonts-freefont-ttf libfreetype6-dev
pip install git+https://github.com/pygame/pygame.git
pip install git+https://github.com/ntasfi/PyGame-Learning-Environment.git
```
Args:
level (string | subclass of `ple.games.base`): Game instance or name of class in
`ple.games`, like 'doom', 'flappybird', 'monsterkong', 'catcher', 'pixelcopter',
'pong', 'puckworld', 'raycastmaze', 'snake', 'waterworld'
(<span style="color:#C00000"><b>required</b></span>).
visualize (bool): Whether to visualize interaction
(<span style="color:#00C000"><b>default</b></span>: false).
frame_skip (int > 0): Number of times to repeat an action without observing
(<span style="color:#00C000"><b>default</b></span>: 1).
fps (int > 0): The desired frames per second we want to run our game at
(<span style="color:#00C000"><b>default</b></span>: 30).
"""
@classmethod
def levels(cls):
import ple
levels = list()
for level in dir(ple.games):
level_cls = getattr(ple.games, level)
if isinstance(level_cls, type) and issubclass(level_cls, ple.games.base.PyGameWrapper):
levels.append(level)
return levels
def __init__(self, level, visualize=False, frame_skip=1, fps=30):
import ple
if isinstance(level, str):
assert level in PyGameLearningEnvironment.levels()
level = getattr(ple.games, level)()
self.environment = ple.PLE(
game=level, fps=fps, frame_skip=frame_skip, display_screen=visualize
# num_steps=1, reward_values={}, force_fps=True, add_noop_action=True, NOOP=K_F15,
# state_preprocessor=None, rng=24
)
self.environment.init()
self.has_game_state = self.getGameStateDims() is not None
self.available_actions = tuple(self.environment.getActionSet())
def __str__(self):
return super().__str__() + '({})'.format(self.environment.__class__.__name__)
def states(self):
if self.has_game_state:
return OrderedDict(
screen=dict(type='float', shape=(tuple(self.environment.getScreenDims()) + (3,))),
state=dict(type='float', shape=(tuple(self.environment.getGameStateDims) + (3,)))
)
else:
return dict(type='float', shape=(tuple(self.environment.getScreenDims()) + (3,)))
def actions(self):
return dict(type='int', shape=(), num_actions=len(self.available_actions))
def close(self):
self.environment = None
def get_states(self):
screen = self.environment.getScreenRGB().astype(dtype=np.float32) / 255.0
if self.has_game_state:
return OrderedDict(screen=screen, state=self.environment.getGameState())
else:
return screen
def reset(self):
self.environment.reset_game()
return self.get_states()
def execute(self, actions):
if self.environment.game_over():
raise TensorforceError.unexpected()
reward = self.environment.act(action=self.available_actions[actions])
terminal = self.environment.game_over()
states = self.get_states()
return states, terminal, reward