def test_custom_port_app(self, mock_unity3d):
"""Test if the base_port + worker_id is different
for each environment when using custom ports"""
_ = Unity3DEnv(file_name="app", port=5010)
args, kwargs_first = mock_unity3d.call_args
_ = Unity3DEnv(file_name="app", port=5010)
args, kwargs_second = mock_unity3d.call_args
self.assertNotEqual(
kwargs_first.get("base_port") + kwargs_first.get("worker_id"),
kwargs_second.get("base_port") + kwargs_second.get("worker_id"))
def test_ports_multi_app(self, mock_unity3d):
"""Test if the base_port + worker_id
is different for each environment"""
_ = Unity3DEnv(file_name="app", port=None)
args, kwargs_first = mock_unity3d.call_args
_ = Unity3DEnv(file_name="app", port=None)
args, kwargs_second = mock_unity3d.call_args
self.assertNotEqual(
kwargs_first.get("base_port") + kwargs_first.get("worker_id"),
kwargs_second.get("base_port") + kwargs_second.get("worker_id"),
)
def test_port_editor(self, mock_unity3d):
"""Test if the environment uses the editor port
when no environment file is provided"""
_ = Unity3DEnv(port=None)
args, kwargs = mock_unity3d.call_args
mock_unity3d.assert_called_once()
self.assertEqual(5004, kwargs.get("base_port"))
def test_port_app(self, mock_unity3d):
"""Test if the environment uses the correct port
when the environment file is provided"""
_ = Unity3DEnv(file_name="app", port=None)
args, kwargs = mock_unity3d.call_args
mock_unity3d.assert_called_once()
self.assertEqual(5005, kwargs.get("base_port"))
# We are remote worker or we are local worker with num_workers=0:
# Create a PolicyServerInput.
if ioctx.worker_index > 0 or ioctx.worker.num_workers == 0:
return PolicyServerInput(
ioctx,
SERVER_ADDRESS,
args.port + ioctx.worker_index -
(1 if ioctx.worker_index > 0 else 0),
)
# No InputReader (PolicyServerInput) needed.
else:
return None
# Get the multi-agent policies dict and agent->policy
# mapping-fn.
policies, policy_mapping_fn = Unity3DEnv.get_policy_configs_for_game(
args.env)
# The entire config will be sent to connecting clients so they can
# build their own samplers (and also Policy objects iff
# `inference_mode=local` on clients' command line).
config = {
# Indicate that the Trainer we setup here doesn't need an actual env.
# Allow spaces to be determined by user (see below).
"env": None,
# Use the `PolicyServerInput` to generate experiences.
"input": _input,
# Use n worker processes to listen on different ports.
"num_workers": args.num_workers,
# Disable OPE, since the rollouts are coming from online clients.
"off_policy_estimation_methods": {},
# Other settings.
)
if __name__ == "__main__":
args = parser.parse_args()
# Start the client for sending environment information (e.g. observations,
# actions) to a policy server (listening on port 9900).
client = PolicyClient(
"http://" + args.server + ":" + str(args.port),
inference_mode=args.inference_mode,
update_interval=args.update_interval_local_mode,
)
# Start and reset the actual Unity3DEnv (either already running Unity3D
# editor or a binary (game) to be started automatically).
env = Unity3DEnv(file_name=args.game, episode_horizon=args.horizon)
obs = env.reset()
eid = client.start_episode(training_enabled=not args.no_train)
# Keep track of the total reward per episode.
total_rewards_this_episode = 0.0
# Loop infinitely through the env.
while True:
# Get actions from the Policy server given our current obs.
actions = client.get_action(eid, obs)
# Apply actions to our env.
obs, rewards, dones, infos = env.step(actions)
total_rewards_this_episode += sum(rewards.values())
# Log rewards and single-agent dones.
client.log_returns(eid, rewards, infos, multiagent_done_dict=dones)
"--horizon",
type=int,
default=3000,
help="The max. number of `step()`s for any episode (per agent) before "
"it'll be reset again automatically.")
parser.add_argument("--torch", action="store_true")
if __name__ == "__main__":
ray.init()
args = parser.parse_args()
tune.register_env(
"unity3d", lambda c: Unity3DEnv(
file_name=c["file_name"],
no_graphics=(args.env != "VisualHallway" and c["file_name"] is
not None),
episode_horizon=c["episode_horizon"],
))
# Get policies (different agent types; "behaviors" in MLAgents) and
# the mappings from individual agents to Policies.
policies, policy_mapping_fn = \
Unity3DEnv.get_policy_configs_for_game(args.env)
config = {
"env": "unity3d",
"env_config": {
"file_name": args.file_name,
"episode_horizon": args.horizon,
},
# For running in editor, force to use just one Worker (we only have
parser.add_argument("--no-restore",
action="store_true",
help="Whether to load the Policy "
"weights from a previous checkpoint")
if __name__ == "__main__":
args = parser.parse_args()
ray.init()
# Create a fake-env for the server. This env will never be used (neither
# for sampling, nor for evaluation) and its obs/action Spaces do not
# matter either (multi-agent config below defines Spaces per Policy).
register_env("fake_unity", lambda c: RandomMultiAgentEnv(c))
policies, policy_mapping_fn = \
Unity3DEnv.get_policy_configs_for_game(args.env)
# The entire config will be sent to connecting clients so they can
# build their own samplers (and also Policy objects iff
# `inference_mode=local` on clients' command line).
config = {
# Use the connector server to generate experiences.
"input":
(lambda ioctx: PolicyServerInput(ioctx, SERVER_ADDRESS, args.port)),
# Use a single worker process (w/ SyncSampler) to run the server.
"num_workers":
0,
# Disable OPE, since the rollouts are coming from online clients.
"input_evaluation": [],
# Other settings.