def setup_class(cls):
# Initialize the environment
env_options = soccer_environment.SoccerEnvironmentOptions(team_size=2)
cls.env = soccer_environment.SoccerEnvironment(env_options=env_options)
# Get the environment state
cls.state = cls.env.state
# Get the agent indexes
cls.player_index = [
cls.env.get_agent_index('PLAYER', team_index)
for team_index in range(2)
]
cls.computer_index = [
cls.env.get_agent_index('COMPUTER', team_index)
for team_index in range(2)
]
def main():
# Initialize the random number generator to have consistent results
random.seed(0)
# Create a soccer environment
soccer_env = soccer_environment.SoccerEnvironment()
# Run many episodes
for episode_index in range(20):
# Print the episode number
print('')
print('Episode {}:'.format(episode_index + 1))
# Reset the environment
observation = soccer_env.reset()
# Print the initial state
print('Initial state:\n{}\n'.format(observation))
# Run the episode
is_running = True
while is_running:
# Render the environment
soccer_env.render()
# Get the screenshot
screenshot = soccer_env.renderer.get_screenshot()
# Get a random action from the action list
action = random.choice(soccer_env.actions)
# Take the action
team_agent_index = 0
agent_index = soccer_env.get_agent_index('PLAYER',
team_agent_index)
soccer_env.take_cached_action(agent_index, action)
# Update the state and get the observation
observation = soccer_env.update_state()
# Check the terminal state
if soccer_env.state.is_terminal():
print('Terminal state:\n{}'.format(observation))
print('Episode {} ends at time step {}'.format(
episode_index + 1, soccer_env.state.time_step + 1))
is_running = False
# Save the last screenshot
soccer_env.render()
screenshot = soccer_env.renderer.get_screenshot()
screenshot_relative_path = 'screenshot.png'
screenshot_abs_path = os.path.abspath(screenshot_relative_path)
scipy.misc.imsave(screenshot_abs_path, screenshot)
print('The last screenshot is saved to {}'.format(screenshot_abs_path))
def main():
# Create a renderer options
renderer_options = soccer_renderer.RendererOptions(show_display=True,
max_fps=60,
enable_key_events=True)
# Create a soccer environment
soccer_env = soccer_environment.SoccerEnvironment(
renderer_options=renderer_options)
# Get the renderer wrapped in the environment
renderer = soccer_env.renderer
# Initialize the renderer
renderer.load()
# Keep rendering until the renderer window is closed
is_running = True
while is_running:
is_running = renderer.render()
def setup_class(cls):
env = soccer_environment.SoccerEnvironment()
cls.renderer = env.renderer
def main():
# Initialize the random number generator to have consistent results
random.seed(0)
# Resolve the map path relative to this file
map_path = file_util.resolve_path(
__file__, '../data/map/soccer/soccer_21x14_goal_4.tmx')
# Create a soccer environment options
# "map_data" is specified to use the custom map.
# "team_size" is given to specify the agents in one team.
# "ai_frame_skip" is to control the frame skip for AI only
env_options = soccer_environment.SoccerEnvironmentOptions(
map_path=map_path, team_size=2, ai_frame_skip=2)
# Create a soccer environment
# If you want to render the environment, an optional argument
# "renderer_options" can be used. For the sample usage, see
# "sample/renderer.py".
env = soccer_environment.SoccerEnvironment(env_options=env_options)
# Run many episodes
for episode_index in range(20):
# Print the episode number
print('')
print('Episode {}:'.format(episode_index + 1))
# Reset the environment and get the initial observation. The observation
# is a class defined as "soccer_environment.SoccerObservation".
observation = env.reset()
state = observation.state
action = observation.action
reward = observation.reward
next_state = observation.next_state
# Print the state, action, reward, and next state pair
print('Initial state:\n({}, {}, {}, {})\n'.format(
state, action, reward, next_state))
# Run the episode
is_running = True
while is_running:
# Render the environment. The renderer will lazy load on the first
# call. Skip the call if you don't need the rendering.
env.render()
# Get the partially observable screenshot of the first agent with a
# radius of 1. The returned `screenshot` is a `numpy.ndarray`, the
# format is the same as the returned value of `scipy.misc.imread`.
# The previous call is required for this call to work.
po_screenshot = env.renderer.get_po_screenshot(0, 1)
# Control only the first agent in each team
team_agent_index = 0
for team_name in env.team_names:
agent_index = env.get_agent_index(team_name, team_agent_index)
action = random.choice(env.actions)
env.take_cached_action(agent_index, action)
# Update the state and get the observation
observation = env.update_state()
# Check the terminal state
if env.state.is_terminal():
print('Terminal state:\n{}'.format(observation))
print('Episode {} ends at time step {}'.format(
episode_index + 1, env.state.time_step + 1))
is_running = False
# Save the last partially observable screenshot
env.render()
agent_pos = np.array(env.state.get_agent_pos(0))
po_screenshot = env.renderer.get_po_screenshot(agent_pos, radius=1)
screenshot_relative_path = 'screenshot.png'
screenshot_abs_path = os.path.abspath(screenshot_relative_path)
scipy.misc.imsave(screenshot_abs_path, po_screenshot)
print('The last partially observable screenshot is saved to {}'.format(
screenshot_abs_path))
def setup_class(cls):
cls.env = soccer_environment.SoccerEnvironment()