def test_setstate(self, seed):
"""Checks setState functionality."""
cfg = config.Config({
'level': 'tests.symmetric',
'game_engine_random_seed': seed
})
env1 = football_env.FootballEnv(cfg)
env2 = football_env.FootballEnv(cfg)
initial_obs = env1.reset()
env2.reset()
initial_state = env1.get_state()
random.seed(seed)
actions = len(football_action_set.get_action_set(cfg))
first_action = random.randint(0, actions - 1)
first_obs, _, _, _ = env1.step(first_action)
_, _, _, _ = env2.step(first_action)
step = 0
limit = 10 if fast_run else 3000
while step < limit:
step += 1
action = random.randint(0, actions - 1)
if step % 10 == 0:
env2.set_state(initial_state)
self.compare_observations(initial_obs, env2.observation())
env2.step(first_action)
self.compare_observations(first_obs, env2.observation())
env2.set_state(env1.get_state())
self.compare_observations(env1.observation(), env2.observation())
_, _, done1, _ = env1.step(action)
_, _, done2, _ = env2.step(action)
self.assertEqual(done1, done2)
if done1:
break
env1.close()
env2.close()
def main(_):
left_players = FLAGS.left_players.split(',') if FLAGS.left_players else ''
right_players = FLAGS.right_players.split(
',') if FLAGS.right_players else ''
assert not (
'agent' in left_players or 'agent' in right_players
), 'Player type \'agent\' can not be used with play_game. Use tfhub player.'
cfg = config.Config({
'action_set': FLAGS.action_set,
'right_players': right_players,
'dump_full_episodes': True,
'left_players': left_players,
'real_time': FLAGS.real_time,
'render': True
})
if FLAGS.level:
cfg['level'] = FLAGS.level
env = football_env.FootballEnv(cfg)
env.reset()
try:
while True:
_, _, done, _ = env.step(None)
if done:
env.reset()
except KeyboardInterrupt:
env.write_dump('shutdown')
exit(1)
def main(_):
with open(FLAGS.trace_file, 'rb') as f:
replay = six.moves.cPickle.load(f)
trace = modify_trace(replay)
fd, temp_path = tempfile.mkstemp(suffix='.dump')
with tf.gfile.Open(temp_path, 'wb') as f:
six.moves.cPickle.dump(trace, f)
assert replay[0]['debug']['frame_cnt'] == 1, (
'Trace does not start from the beginning of the episode, can not replay')
cfg = config.Config(replay[0]['debug']['config'])
player_type = 'replay={}'.format(temp_path)
cfg['home_players'] = [player_type] * len(cfg['home_players'])
cfg['away_players'] = [player_type] * len(cfg['away_players'])
cfg.update({
'physics_steps_per_frame': int(100 / FLAGS.fps),
'real_time': False,
'render': True,
'tracesdir': '/tmp/dumps',
'write_video': True
})
env = football_env.FootballEnv(cfg)
env.reset(cfg)
done = False
try:
while not done:
_, _, done, _ = env.step(None)
except KeyboardInterrupt:
env.write_dump('shutdown')
exit(1)
os.close(fd)
def replay(self, dump, fps=10, config_update={}, directory=None):
with open(dump, 'rb') as f:
replay = six.moves.cPickle.load(f)
trace = self.__modify_trace(replay, fps)
fd, temp_path = tempfile.mkstemp(suffix='.dump')
with open(temp_path, 'wb') as f:
six.moves.cPickle.dump(trace, f)
assert replay[0]['debug']['frame_cnt'] == 1, (
'Trace does not start from the beginning of the episode, can not replay'
)
cfg = config.Config(replay[0]['debug']['config'])
cfg['players'] = self.__build_players(temp_path, cfg['players'])
config_update['physics_steps_per_frame'] = int(100 / fps)
config_update['real_time'] = False
if 'render' not in config_update:
config_update['render'] = True
if directory:
config_update['tracesdir'] = directory
config_update['write_video'] = True
cfg.update(config_update)
env = football_env.FootballEnv(cfg)
env.reset()
done = False
try:
while not done:
_, _, done, _ = env.step(None)
except KeyboardInterrupt:
env.write_dump('shutdown')
exit(1)
os.close(fd)
def test_corner(self, episode, factor, reverse):
cfg = config.Config({
'level': 'tests.corner_test',
'players': ['agent:left_players=1,right_players=1'],
'episode_number': episode,
'reverse_team_processing': reverse,
})
env = football_env.FootballEnv(cfg)
o = env.reset()
done = False
while not done:
o, _, done, _ = env.step([
football_action_set.action_left,
football_action_set.action_left
])
self.assertAlmostEqual(o[0]['ball'][0], -0.95 * factor, delta=0.1)
self.assertAlmostEqual(o[0]['ball'][1], 0.4 * factor, delta=0.1)
self.assertAlmostEqual(o[0]['right_team'][0][0], 1, delta=0.1)
self.assertAlmostEqual(o[0]['right_team'][1][0],
-0.95 * factor,
delta=0.1)
self.assertAlmostEqual(o[0]['left_team'][0][0], -0.95, delta=0.1)
self.assertAlmostEqual(o[0]['left_team'][1][0],
-0.9 * factor,
delta=0.2)
env.close()
def main(_):
players = FLAGS.players.split(';') if FLAGS.players else ''
assert not (any(['agent' in player for player in players])
), ('Player type \'agent\' can not be used with play_game.')
cfg = config.Config({
'action_set': FLAGS.action_set,
'dump_full_episodes': True,
'players': players,
'real_time': FLAGS.real_time,
})
if FLAGS.level:
cfg['level'] = FLAGS.level
env = football_env.FootballEnv(cfg)
if FLAGS.render:
env.render()
env.reset()
try:
while True:
_, _, done, _ = env.step([])
if done:
env.reset()
except KeyboardInterrupt:
logging.warning('Game stopped, writing dump...')
env.write_dump('shutdown')
exit(1)
def main(_):
cfg = config.Config({
'action_set':
FLAGS.action_set,
'away_players':
FLAGS.away_players.split(',') if FLAGS.away_players else '',
'dump_full_episodes':
True,
'home_players':
FLAGS.home_players.split(',') if FLAGS.home_players else '',
'real_time':
FLAGS.real_time,
'render':
True
})
if FLAGS.level:
cfg['level'] = FLAGS.level
env = football_env.FootballEnv(cfg)
env.reset(cfg)
try:
while True:
_, _, done, _ = env.step(None)
if done:
env.reset(cfg)
except KeyboardInterrupt:
env.write_dump('shutdown')
exit(1)
def run_scenario(cfg, queue, actions, render=False, validation=True):
env = football_env.FootballEnv(cfg)
if render:
env.render()
obs = env.reset()
queue.put(obs)
if validation:
env.tracker_setup(0, 999999999999999)
done = False
step = 0
while True:
if isinstance(actions, Iterable):
if step >= len(actions):
break
action = actions[step]
else:
action = actions.get()
if action is None:
break
step += 1
if isinstance(action, Iterable):
obs, _, done, _ = env.step(action)
else:
obs, _, done, _ = env.step([action, action])
queue.put(obs)
if done:
break
queue.put(None)
env.close()
def generate_replay(self):
"""Generates replay of an episode."""
cfg = config.Config()
left_players = 2
cfg.update({
'action_set':
'full',
'level':
'tests.corner_test',
'dump_full_episodes':
True,
'players': [
'agent:left_players={}'.format(left_players),
'bot:right_players=1', 'lazy:right_players=1'
],
'tracesdir':
test_tmpdir
})
env = football_env.FootballEnv(cfg)
env.reset()
actions_cnt = len(football_action_set.get_action_set(cfg))
done = False
step = 0
while not done:
step += 1
actions = [(step + x) % actions_cnt for x in range(left_players)]
_, _, done, _ = env.step(actions)
env.close()
def test_different_action_formats(self): """Verify different action formats are accepted.""" cfg = config.Config() env = football_env.FootballEnv(cfg) env.reset() env.step(football_action_set.action_right) env.step([football_action_set.action_right]) env.step(np.array([football_action_set.action_right])) env.step(np.array(football_action_set.action_right)) env.close()
def test_player_order_invariant(self):
"""Checks that environment behaves the same regardless of players order."""
players = ['agent:right_players=1', 'lazy:left_players=11']
cfg = config.Config({
'level': 'tests.11_vs_11_hard_deterministic',
'players': players
})
env = football_env.FootballEnv(cfg)
actions = len(football_action_set.get_action_set(cfg))
hash_value1 = compute_hash(env, actions)
players = [players[1], players[0]]
cfg = config.Config({
'level': 'tests.11_vs_11_hard_deterministic',
'players': players
})
env = football_env.FootballEnv(cfg)
hash_value2 = compute_hash(env, actions)
self.assertEqual(hash_value1, hash_value2)
env.close()
def test_multi_render(self):
"""Only one rendering instance allowed at a time."""
if 'UNITTEST_IN_DOCKER' in os.environ:
# Rendering is not supported.
return
cfg = config.Config({})
env1 = football_env.FootballEnv(cfg)
env1.render()
env1.reset()
env2 = football_env.FootballEnv(cfg)
try:
env2.render()
except AssertionError:
env1.close()
env2.close()
# It is still possible to render.
env3 = football_env.FootballEnv(cfg)
env3.reset()
env3.close()
return
assert False, 'Exception expected'
def test_restore_after_done(self):
cfg = config.Config({
'level': 'academy_empty_goal_close',
})
env = football_env.FootballEnv(cfg)
env.reset()
state = env.get_state()
# Go right until reaching the goal.
done = False
while not done:
_, _, done, _ = env.step(5)
env.set_state(state)
env.step(0) # Test if can take step
def test_restore_after_reset(self):
cfg = config.Config({
'level': '11_vs_11_competition',
})
env = football_env.FootballEnv(cfg)
obs = env.reset()
state = env.get_state()
env.reset()
env.set_state(state)
obs_ = env.observation()
state_ = env.get_state()
env.step(0) # Test if can take step
self.compare_observations(obs, obs_)
self.assertEqual(state, state_)
def run_scenario(cfg, seed, queue, actions, render=False, validation=True):
env = football_env.FootballEnv(cfg)
if render:
env.render()
env.reset()
if validation:
env.tracker_setup(0, 999999999999999)
done = False
for action in actions:
obs, _, done, _ = env.step([action, action])
queue.put(obs)
if done:
break
queue.put(None)
env.close()
def test__memory_usage(self):
"""Make sure memory usage is low when not recording videos."""
# This test has to go first, so that memory usage is not affected.
if 'UNITTEST_IN_DOCKER' in os.environ:
# Forge doesn't support rendering.
return
cfg = config.Config({'write_video': False})
env = football_env.FootballEnv(cfg)
env.render()
env.reset()
initial_memory = self.memory_usage()
for _ in range(100):
_, _, _, _ = env.step(football_action_set.action_right)
memory_usage = self.memory_usage() - initial_memory
env.close()
self.assertGreaterEqual(10000000, memory_usage)
def test_second_half(self):
"""Test second half feature."""
cfg = config.Config()
cfg['level'] = 'tests.second_half'
env = football_env.FootballEnv(cfg)
for _ in range(5):
o, _, done, _ = env.step(football_action_set.action_idle)
self.assertFalse(done)
self.assertAlmostEqual(o[0]['left_team'][o[0]['active']][0], 0, delta=0.1)
for _ in range(6):
self.assertFalse(done)
o, _, done, _ = env.step(football_action_set.action_idle)
self.assertAlmostEqual(
o[0]['left_team'][o[0]['active']][0], -0.5, delta=0.1)
self.assertTrue(done)
env.close()
def check_determinism(self, extensive=False):
"""Check that environment is deterministic."""
if 'UNITTEST_IN_DOCKER' in os.environ:
return
cfg = config.Config({'level': 'tests.11_vs_11_hard_deterministic'})
env = football_env.FootballEnv(cfg)
actions = len(football_action_set.get_action_set(cfg))
for episode in range(1 if extensive else 2):
hash_value = compute_hash(env, actions, extensive)
if extensive:
self.assertEqual(hash_value, 4203104251)
elif episode % 2 == 0:
self.assertEqual(hash_value, 716323440)
else:
self.assertEqual(hash_value, 1663893701)
env.close()
def test___render(self):
"""Make sure rendering is not broken."""
if 'UNITTEST_IN_DOCKER' in os.environ:
# Rendering is not supported.
return
cfg = config.Config({
'level': 'tests.11_vs_11_hard_deterministic',
})
env = football_env.FootballEnv(cfg)
env.render()
o = env.reset()
hash = observation_hash(o)
for _ in range(10):
o, _, _, _ = env.step(football_action_set.action_right)
hash = observation_hash(o, hash)
self.assertEqual(hash, 2763980076)
env.close()
def test_goal(self, episode, reverse):
cfg = config.Config({
'level': 'tests.goal_test',
'players': ['agent:left_players=1,right_players=1'],
'episode_number': episode,
'reverse_team_processing': reverse,
})
env = football_env.FootballEnv(cfg)
o = env.reset()
done = False
while not done:
o, _, done, _ = env.step(
[football_action_set.action_right, football_action_set.action_right])
self.assertAlmostEqual(o[0]['ball'][0], 0.0, delta=0.1)
self.assertEqual(o[0]['score'][episode], 1)
self.assertEqual(o[0]['score'][1 - episode], 0)
env.close()
def test_penalty(self):
cfg = config.Config({
'level': 'tests.penalty',
'players': ['agent:left_players=1'],
})
env = football_env.FootballEnv(cfg)
o = env.reset()
done = False
while not done:
o, _, done, _ = env.step([football_action_set.action_sliding])
self.assertAlmostEqual(o[0]['ball'][0], -0.809, delta=0.01)
self.assertAlmostEqual(o[0]['ball'][1], 0.0, delta=0.01)
self.assertAlmostEqual(o[0]['right_team'][0][0], 1, delta=0.1)
self.assertAlmostEqual(o[0]['right_team'][1][0], -0.75, delta=0.1)
self.assertAlmostEqual(o[0]['left_team'][0][0], -0.95, delta=0.1)
self.assertAlmostEqual(o[0]['left_team'][1][0], -0.70, delta=0.1)
env.close()
def test_offside(self, episode, team2, reverse):
cfg = config.Config({
'level': 'tests.offside_test',
'players': ['agent:{}_players=1'.format(team2)],
'episode_number': episode,
'reverse_team_processing': reverse,
})
env = football_env.FootballEnv(cfg)
env.reset()
o, _, done, _ = env.step(football_action_set.action_long_pass)
done = False
while not done and o[0]['right_team'][1][0] == 0:
o, _, done, _ = env.step(football_action_set.action_idle)
self.assertAlmostEqual(o[0]['ball'][0], 0.6, delta=0.4)
self.assertAlmostEqual(o[0]['right_team'][0][0], 0.6, delta=0.4)
self.assertAlmostEqual(o[0]['right_team'][1][0], 0.6, delta=0.4)
self.assertAlmostEqual(o[0]['left_team'][0][0], -0.6, delta=0.4)
self.assertAlmostEqual(o[0]['left_team'][1][0], -0.6, delta=0.4)
env.close()
def create_multiagent_env(iprocess):
left_player = 'ppo2_cnn:left_players=1,policy=gfootball_impala_cnn,checkpoint=/Users/stephen/Documents/football/checkpoints/11_vs_11_easy_stochastic_v2'
right_player = 'agent:right_players=1,policy=gfootball_impala_cnn,checkpoint=/Users/stephen/Documents/football/checkpoints/11_vs_11_easy_stochastic_v2'
players = [left_player, right_player]
write_full_episode_dumps = False and (iprocess == 0)
write_goal_dumps = False and (iprocess == 0)
config_values = {
'dump_full_episodes': write_full_episode_dumps,
'dump_scores': write_goal_dumps,
'players': players,
'level': '11_vs_11_easy_stochastic',
'tracesdir': '', # logdir
'write_video': False
}
cfg = config.Config(config_values)
env = football_env.FootballEnv(cfg)
render = False and (iprocess == 0)
if render:
env.render()
dump_frequency = 10 if render and iprocess == 0 else 0
env = wrappers.PeriodicDumpWriter(env, dump_frequency)
rewards = 'scoring,checkpoints' # what to base rewards on
representation = 'extracted' # ['simple115v2'] what observations model gets
channel_dimensions = (observation_preprocessing.SMM_WIDTH,
observation_preprocessing.SMM_HEIGHT)
apply_single_agent_wrappers = True
stacked = True # whether to get last 4 observations stacked or just last 1
env = _apply_output_wrappers(env, rewards, representation,
channel_dimensions,
apply_single_agent_wrappers, stacked)
env = monitor.Monitor(
env,
logger.get_dir() and os.path.join(logger.get_dir(), str(iprocess)))
return env
def main(_):
left_player = 'ppo2_cnn:left_players=1,policy=gfootball_impala_cnn,checkpoint=/Users/stephen/Documents/football/checkpoints/11_vs_11_easy_stochastic_v2'
right_player = 'ppo2_cnn:right_players=1,policy=gfootball_impala_cnn,checkpoint=/Users/stephen/Documents/football/checkpoints/11_vs_11_easy_stochastic_v2'
players = [left_player, right_player]
config_values = {'dump_full_episodes': False,
'dump_scores': False,
'players': players,
'level': '11_vs_11_easy_stochastic',
'tracesdir': '/Users/stephen/Documents/football/logs', # logdir
'write_video': False}
cfg = config.Config(config_values)
env = football_env.FootballEnv(cfg)
render = False
if render:
env.render()
env.reset()
dump_frequency = 3
env = wrappers.PeriodicDumpWriter(env, dump_frequency)
n_timesteps = int(2 * 3e3 + 1) # 3k per episode
right_agent_ep_scores = []
ep_right_scores = 0.0
for _ in range(n_timesteps):
_, reward, done, _ = env.step([])
ep_right_scores -= reward
if done:
right_agent_ep_scores.append(ep_right_scores)
ep_right_scores = 0.0
env.reset()
mean_score = sum(right_agent_ep_scores) / len(right_agent_ep_scores)
print(f'\n***\nRight agent episode scores: {right_agent_ep_scores}\n' +
f'Right agent episode mean score: {mean_score}\n***\n')
def test_dynamic_render(self):
"""Verifies dynamic render support."""
if 'UNITTEST_IN_DOCKER' in os.environ:
# Rendering is not supported.
return
cfg = config.Config({
'level': 'tests.11_vs_11_hard_deterministic',
})
env = football_env.FootballEnv(cfg)
o = env.reset()
for _ in range(10):
o, _, _, _ = env.step(football_action_set.action_right)
self.assertNotIn('frame', o[0])
env.render()
self.assertIn('frame', env.observation()[0])
self.compare_observations(o, env.observation())
o, _, _, _ = env.step(football_action_set.action_right)
self.assertIn('frame', env.observation()[0])
env.disable_render()
self.compare_observations(o, env.observation())
env.close()
def test_score_empty_goal(self):
"""Score on an empty goal."""
cfg = config.Config()
env = football_env.FootballEnv(cfg)
cfg['level'] = 'academy_empty_goal'
last_o = env.reset()[0]
for _ in range(120):
o, reward, done, _ = env.step(football_action_set.action_right)
o = o[0]
if done:
self.assertEqual(reward, 1)
break
self.assertFalse(done)
self.assertGreaterEqual(o['ball'][0], last_o['ball'][0] - 0.01)
self.assertGreaterEqual(
o['left_team'][o['active']][0],
last_o['left_team'][last_o['active']][0] - 0.01)
last_o = o
self.assertTrue(done)
env.close()
def check_determinism(self, extensive=False):
"""Check that environment is deterministic."""
if 'UNITTEST_IN_DOCKER' in os.environ:
return
cfg = config.Config({'level': 'tests.11_vs_11_hard_deterministic'})
env = football_env.FootballEnv(cfg)
actions = len(football_action_set.get_action_set(cfg))
for episode in range(1 if extensive else 2):
hash_value = compute_hash(env, actions, extensive)
if extensive:
if hash_value != 1174966789:
self.assertEqual(hash_value, 2245893576)
elif episode % 2 == 0:
if hash_value != 2275067030:
self.assertEqual(hash_value, 4024823270)
else:
if hash_value != 2045063811:
self.assertEqual(hash_value, 1264083657)
env.close()
def create_environment(
env_name='',
stacked=False,
representation='extracted',
rewards='scoring',
write_goal_dumps=False,
write_full_episode_dumps=False,
render=False,
write_video=False,
dump_frequency=1,
logdir='',
extra_players=None,
number_of_left_players_agent_controls=1,
number_of_right_players_agent_controls=0,
channel_dimensions=(observation_preprocessing.SMM_WIDTH,
observation_preprocessing.SMM_HEIGHT),
other_config_options={}):
"""Creates a Google Research Football environment.
Args:
env_name: a name of a scenario to run, e.g. "11_vs_11_stochastic".
The list of scenarios can be found in directory "scenarios".
stacked: If True, stack 4 observations, otherwise, only the last
observation is returned by the environment.
Stacking is only possible when representation is one of the following:
"pixels", "pixels_gray" or "extracted".
In that case, the stacking is done along the last (i.e. channel)
dimension.
representation: String to define the representation used to build
the observation. It can be one of the following:
'pixels': the observation is the rendered view of the football field
downsampled to 'channel_dimensions'. The observation size is:
'channel_dimensions'x3 (or 'channel_dimensions'x12 when "stacked" is
True).
'pixels_gray': the observation is the rendered view of the football field
in gray scale and downsampled to 'channel_dimensions'. The observation
size is 'channel_dimensions'x1 (or 'channel_dimensions'x4 when stacked
is True).
'extracted': also referred to as super minimap. The observation is
composed of 4 planes of size 'channel_dimensions'.
Its size is then 'channel_dimensions'x4 (or 'channel_dimensions'x16 when
stacked is True).
The first plane P holds the position of players on the left
team, P[y,x] is 255 if there is a player at position (x,y), otherwise,
its value is 0.
The second plane holds in the same way the position of players
on the right team.
The third plane holds the position of the ball.
The last plane holds the active player.
'simple115'/'simple115v2': the observation is a vector of size 115.
It holds:
- the ball_position and the ball_direction as (x,y,z)
- one hot encoding of who controls the ball.
[1, 0, 0]: nobody, [0, 1, 0]: left team, [0, 0, 1]: right team.
- one hot encoding of size 11 to indicate who is the active player
in the left team.
- 11 (x,y) positions for each player of the left team.
- 11 (x,y) motion vectors for each player of the left team.
- 11 (x,y) positions for each player of the right team.
- 11 (x,y) motion vectors for each player of the right team.
- one hot encoding of the game mode. Vector of size 7 with the
following meaning:
{NormalMode, KickOffMode, GoalKickMode, FreeKickMode,
CornerMode, ThrowInMode, PenaltyMode}.
Can only be used when the scenario is a flavor of normal game
(i.e. 11 versus 11 players).
rewards: Comma separated list of rewards to be added.
Currently supported rewards are 'scoring' and 'checkpoints'.
write_goal_dumps: whether to dump traces up to 200 frames before goals.
write_full_episode_dumps: whether to dump traces for every episode.
render: whether to render game frames.
Must be enable when rendering videos or when using pixels
representation.
write_video: whether to dump videos when a trace is dumped.
dump_frequency: how often to write dumps/videos (in terms of # of episodes)
Sub-sample the episodes for which we dump videos to save some disk space.
logdir: directory holding the logs.
extra_players: A list of extra players to use in the environment.
Each player is defined by a string like:
"$player_name:left_players=?,right_players=?,$param1=?,$param2=?...."
number_of_left_players_agent_controls: Number of left players an agent
controls.
number_of_right_players_agent_controls: Number of right players an agent
controls.
channel_dimensions: (width, height) tuple that represents the dimensions of
SMM or pixels representation.
other_config_options: dict that allows directly setting other options in
the Config
Returns:
Google Research Football environment.
"""
assert env_name
scenario_config = config.Config({'level': env_name}).ScenarioConfig()
players = [('agent:left_players=%d,right_players=%d' %
(number_of_left_players_agent_controls,
number_of_right_players_agent_controls))]
# Enable MultiAgentToSingleAgent wrapper?
multiagent_to_singleagent = False
if scenario_config.control_all_players:
if (number_of_left_players_agent_controls in [0, 1]
and number_of_right_players_agent_controls in [0, 1]):
multiagent_to_singleagent = True
players = [('agent:left_players=%d,right_players=%d' %
(scenario_config.controllable_left_players
if number_of_left_players_agent_controls else 0,
scenario_config.controllable_right_players
if number_of_right_players_agent_controls else 0))]
if extra_players is not None:
players.extend(extra_players)
config_values = {
'dump_full_episodes': write_full_episode_dumps,
'dump_scores': write_goal_dumps,
'players': players,
'level': env_name,
'tracesdir': logdir,
'write_video': write_video,
}
config_values.update(other_config_options)
c = config.Config(config_values)
env = football_env.FootballEnv(c)
if multiagent_to_singleagent:
env = wrappers.MultiAgentToSingleAgent(
env, number_of_left_players_agent_controls,
number_of_right_players_agent_controls)
if dump_frequency > 1:
env = wrappers.PeriodicDumpWriter(env, dump_frequency, render)
elif render:
env.render()
env = _apply_output_wrappers(env, rewards, representation,
channel_dimensions,
(number_of_left_players_agent_controls +
number_of_right_players_agent_controls == 1),
stacked)
return env
def create_environment(env_name='',
stacked=False,
representation='extracted',
with_checkpoints=False,
enable_goal_videos=False,
enable_full_episode_videos=False,
render=False,
write_video=False,
dump_frequency=1,
logdir='',
data_dir=None,
font_file=None,
away_player=None):
"""Creates a Google Research Football environment.
Args:
env_name: a name of a scenario to run, e.g. "11_vs_11_stochastic".
The list of scenarios can be found in directory "scenarios".
stacked: If True, stack 4 observations, otherwise, only the last
observation is returned by the environment.
Stacking is only possible when representation is one of the following:
"pixels", "pixels_gray" or "extracted".
In that case, the stacking is done along the last (i.e. channel)
dimension.
representation: String to define the representation used to build
the observation. It can be one of the following:
'pixels': the observation is the rendered view of the football field
downsampled to w=96, h=72. The observation size is: 72x96x3
(or 72x96x12 when "stacked" is True).
'pixels_gray': the observation is the rendered view of the football field
in gray scale and downsampled to w=96, h=72. The observation size is
72x96x1 (or 72x96x4 when stacked is True).
'extracted': also referred to as super minimap. The observation is
composed of 4 planes of size w=96, h=72.
Its size is then 72x96x4 (or 72x96x16 when stacked is True).
The first plane P holds the position of the 11 player of the home
team, P[y,x] is one if there is a player at position (x,y), otherwise,
its value is zero.
The second plane holds in the same way the position of the 11 players
of the away team.
The third plane holds the active player of the home team.
The last plane holds the position of the ball.
'simple115': the observation is a vector of size 115. It holds:
- the ball_position and the ball_direction as (x,y,z)
- one hot encoding of who controls the ball.
[1, 0, 0]: nobody, [0, 1, 0]: home team, [0, 0, 1]: away team.
- one hot encoding of size 11 to indicate who is the active player
in the home team.
- 11 (x,y) positions for each player of the home team.
- 11 (x,y) motion vectors for each player of the home team.
- 11 (x,y) positions for each player of the away team.
- 11 (x,y) motion vectors for each player of the away team.
- one hot encoding of the game mode. Vector of size 7 with the
following meaning:
{NormalMode, KickOffMode, GoalKickMode, FreeKickMode,
CornerMode, ThrowInMode, PenaltyMode}.
Can only be used when the scenario is a flavor of normal game
(i.e. 11 versus 11 players).
with_checkpoints: True to add intermediate checkpoint rewards to guide
the agent to move to the opponent goal.
If False, only scoring provides a reward.
enable_goal_videos: whether to dump traces up to 200 frames before goals.
enable_full_episode_videos: whether to dump traces for every episode.
render: whether to render game frames.
Must be enable when rendering videos or when using pixels
representation.
write_video: whether to dump videos when a trace is dumped.
dump_frequency: how often to write dumps/videos (in terms of # of episodes)
Sub-sample the episodes for which we dump videos to save some disk space.
logdir: directory holding the logs.
data_dir: location of the game engine data
Safe to leave as the default value.
font_file: location of the game font file
Safe to leave as the default value.
away_player: Away player (adversary) to use in the environment.
Reserved for future usage to provide an opponent to train against.
(which could be used for self-play).
Returns:
Google Research Football environment.
"""
assert env_name
away_players = [away_player] if away_player else []
c = config.Config({
'dump_full_episodes': enable_full_episode_videos,
'dump_scores': enable_goal_videos,
'level': env_name,
'render': render,
'tracesdir': logdir,
'write_video': write_video,
'away_players': away_players,
})
if data_dir:
c['data_dir'] = data_dir
if font_file:
c['font_file'] = font_file
env = football_env.FootballEnv(c)
if dump_frequency > 1:
env = wrappers.PeriodicDumpWriter(env, dump_frequency)
if with_checkpoints:
env = wrappers.CheckpointRewardWrapper(env)
if representation.startswith('pixels'):
env = wrappers.PixelsStateWrapper(env, 'gray' in representation)
elif representation == 'simple21':
env = wrappers.Simple21StateWrapper(env)
elif representation == 'simple115':
env = wrappers.Simple115StateWrapper(env)
elif representation == 'extracted':
env = wrappers.SMMWrapper(env)
else:
raise ValueError(
'Unsupported representation: {}'.format(representation))
if stacked:
env = FrameStack(env, 4)
return env
def main():
args = parse_args()
players = args.players.split(';')
config = Config({
'action_set': ActionSetType.DEFAULT,
'dump_full_episodes': False,
'players': players,
# 'real_time': args.real_time and args.render,
'real_time': args.render and (not args.video),
'pitch_scale': args.pitch_scale,
})
base_player_config = {
'policy_config':
PolicyConfig(
policy_type=args.policy_type,
checkpoint=args.checkpoint,
random_frac=args.random_frac,
action_set=DEFAULT_ACTION_SET,
lr=args.lr,
discount=args.discount,
n_steps=args.n_steps,
verbose=args.verbose,
),
'warmstart':
args.warmstart,
'verbose':
args.verbose,
'video':
args.video,
}
if args.level:
config['level'] = args.level
checkpoint = 'agents/' + args.policy_type.value.lower() + '/agent.npz'
assert not os.system('mkdir -p %s' % os.path.dirname(checkpoint))
env = football_env.FootballEnv(config=config,
base_player_config=base_player_config)
if args.render:
env.render()
obs_history = [
env.reset(), # Need this to know the initial state
]
# self_play_history = History(max_size=int(1e7))
running_score_update = 0.999
running_score = [0, 0, 0]
record = [0, 0, 0]
try:
game_num = 0
epoch_history = []
# cnts_by_mode = defaultdict(int)
while True:
obs, reward, done, info = env.step()
# _, old_relative_obs = env.get_players_and_relative_obs_pairs(obs=obs_history[-1])
# _, new_relative_obs = env.get_players_and_relative_obs_pairs(obs=obs)
if env._agent.num_controlled_right_players() > 0:
reward *= -1
item = HistoryItem(
old_state=obs_history[-1],
action=info['agent_action'],
new_state=obs,
reward=reward.item(),
)
epoch_history.append(item)
# env._agent.give_reward(item=item)
# self_play_history.add(item=item)
# cnts_by_mode[(obs[0]['game_mode'], obs[0]['ball_owned_team'])] += 1
obs_history.append(obs)
if args.verbose:
print(reward, done, info)
if done:
# defaultdict(<class 'int'>, {(0, -1): 36256, (0, 0): 12701, (0, 1): 55352, (2, -1): 1871, (3, -1): 2146, (5, 1): 140, (5, 0): 19269, (4, -1): 1119, (5, -1): 1, (6, -1): 145})
# print(cnts_by_mode)
game_num += 1
score = obs[0]['score']
running_score[0] = running_score_update * running_score[0] + (
1.0 - running_score_update) * score[0]
running_score[1] = running_score_update * running_score[1] + (
1.0 - running_score_update) * score[1]
running_score[2] = running_score[0] - running_score[1]
if score[0] > score[1]:
record[0] += 1
elif score[0] < score[1]:
record[2] += 1
else:
record[1] += 1
# mean_reward = self_play_history.mean_reward()
print(
'Final Score:',
score,
'Running score: [%.3f, %.3f, %.3f]' % tuple([
x / (1 - running_score_update**game_num)
for x in running_score
]),
'Record:',
record,
# 'Mean Reward in history:', mean_reward,
)
# for item in self_play_history.sample(n=int(1e3)):
# env._agent.give_reward(item=item) # ._replace(reward=item.reward - mean_reward))
env._agent.process_epoch(items=epoch_history)
env._agent.reset()
obs_history.append(env.reset())
epoch_history = []
if (not args.render) and (game_num % 25 == 0):
env._agent.save(checkpoint=checkpoint)
if game_num == args.num_games:
break
except KeyboardInterrupt:
logging.warning('Game stopped, writing dump...')
if (not args.render):
env._agent.save(checkpoint='agent.pkl')
# env.write_dump('shutdown')
# return env._agent
print(checkpoint)
exit(1)
