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extends Node
# --fixed-fps 2000 --disable-render-loop
@export_range(1, 10, 1, "or_greater") var action_repeat := 8
@export_range(1, 10, 1, "or_greater") var speed_up = 1
@export var onnx_model_path := ""
@onready var start_time = Time.get_ticks_msec()
const MAJOR_VERSION := "0"
const MINOR_VERSION := "3"
const DEFAULT_PORT := "11008"
const DEFAULT_SEED := "1"
var stream : StreamPeerTCP = null
var connected = false
var message_center
var should_connect = true
var agents
var need_to_send_obs = false
var args = null
var initialized = false
var just_reset = false
var onnx_model = null
var n_action_steps = 0
var _action_space : Dictionary
var _obs_space : Dictionary
# Called when the node enters the scene tree for the first time.
func _ready():
await get_tree().root.ready
get_tree().set_pause(true)
_initialize()
await get_tree().create_timer(1.0).timeout
get_tree().set_pause(false)
func _initialize():
_get_agents()
_obs_space = agents[0].get_obs_space()
_action_space = agents[0].get_action_space()
args = _get_args()
Engine.physics_ticks_per_second = _get_speedup() * 60 # Replace with function body.
Engine.time_scale = _get_speedup() * 1.0
prints("physics ticks", Engine.physics_ticks_per_second, Engine.time_scale, _get_speedup(), speed_up)
# Run inference if onnx model path is set, otherwise wait for server connection
var run_onnx_model_inference : bool = onnx_model_path != ""
if run_onnx_model_inference:
assert(FileAccess.file_exists(onnx_model_path), "Onnx Model Path set on Sync node does not exist: " + onnx_model_path)
onnx_model = ONNXModel.new(onnx_model_path, 1)
_set_heuristic("model")
else:
connected = connect_to_server()
if connected:
_set_heuristic("model")
_handshake()
_send_env_info()
else:
_set_heuristic("human")
_set_seed()
_set_action_repeat()
initialized = true
func _physics_process(delta):
# two modes, human control, agent control
# pause tree, send obs, get actions, set actions, unpause tree
if n_action_steps % action_repeat != 0:
n_action_steps += 1
return
n_action_steps += 1
if connected:
get_tree().set_pause(true)
if just_reset:
just_reset = false
var obs = _get_obs_from_agents()
var reply = {
"type": "reset",
"obs": obs
}
_send_dict_as_json_message(reply)
# this should go straight to getting the action and setting it checked the agent, no need to perform one phyics tick
get_tree().set_pause(false)
return
if need_to_send_obs:
need_to_send_obs = false
var reward = _get_reward_from_agents()
var done = _get_done_from_agents()
#_reset_agents_if_done() # this ensures the new observation is from the next env instance : NEEDS REFACTOR
var obs = _get_obs_from_agents()
var reply = {
"type": "step",
"obs": obs,
"reward": reward,
"done": done
}
_send_dict_as_json_message(reply)
var handled = handle_message()
elif onnx_model != null:
var obs : Array = _get_obs_from_agents()
var actions = []
for o in obs:
var action = onnx_model.run_inference(o["obs"], 1.0)
action["output"] = clamp_array(action["output"], -1.0, 1.0)
var action_dict = _extract_action_dict(action["output"])
actions.append(action_dict)
_set_agent_actions(actions)
need_to_send_obs = true
get_tree().set_pause(false)
_reset_agents_if_done()
else:
_reset_agents_if_done()
func _extract_action_dict(action_array: Array):
var index = 0
var result = {}
for key in _action_space.keys():
var size = _action_space[key]["size"]
if _action_space[key]["action_type"] == "discrete":
result[key] = round(action_array[index])
else:
result[key] = action_array.slice(index,index+size)
index += size
return result
func _get_agents():
agents = get_tree().get_nodes_in_group("AGENT")
func _set_heuristic(heuristic):
for agent in agents:
agent.set_heuristic(heuristic)
func _handshake():
print("performing handshake")
var json_dict = _get_dict_json_message()
assert(json_dict["type"] == "handshake")
var major_version = json_dict["major_version"]
var minor_version = json_dict["minor_version"]
if major_version != MAJOR_VERSION:
print("WARNING: major verison mismatch ", major_version, " ", MAJOR_VERSION)
if minor_version != MINOR_VERSION:
print("WARNING: minor verison mismatch ", minor_version, " ", MINOR_VERSION)
print("handshake complete")
func _get_dict_json_message():
# returns a dictionary from of the most recent message
# this is not waiting
while stream.get_available_bytes() == 0:
stream.poll()
if stream.get_status() != 2:
print("server disconnected status, closing")
get_tree().quit()
return null
OS.delay_usec(10)
var message = stream.get_string()
var json_data = JSON.parse_string(message)
return json_data
func _send_dict_as_json_message(dict):
stream.put_string(JSON.stringify(dict))
func _send_env_info():
var json_dict = _get_dict_json_message()
assert(json_dict["type"] == "env_info")
var message = {
"type" : "env_info",
"observation_space": _obs_space,
"action_space":_action_space,
"n_agents": len(agents)
}
_send_dict_as_json_message(message)
func connect_to_server():
print("Waiting for one second to allow server to start")
OS.delay_msec(1000)
print("trying to connect to server")
stream = StreamPeerTCP.new()
# "localhost" was not working on windows VM, had to use the IP
var ip = "127.0.0.1"
var port = _get_port()
var connect = stream.connect_to_host(ip, port)
stream.set_no_delay(true) # TODO check if this improves performance or not
stream.poll()
# Fetch the status until it is either connected (2) or failed to connect (3)
while stream.get_status() < 2:
stream.poll()
return stream.get_status() == 2
func _get_args():
print("getting command line arguments")
var arguments = {}
for argument in OS.get_cmdline_args():
print(argument)
if argument.find("=") > -1:
var key_value = argument.split("=")
arguments[key_value[0].lstrip("--")] = key_value[1]
else:
# Options without an argument will be present in the dictionary,
# with the value set to an empty string.
arguments[argument.lstrip("--")] = ""
return arguments
func _get_speedup():
print(args)
return args.get("speedup", str(speed_up)).to_int()
func _get_port():
return args.get("port", DEFAULT_PORT).to_int()
func _set_seed():
var _seed = args.get("env_seed", DEFAULT_SEED).to_int()
seed(_seed)
func _set_action_repeat():
action_repeat = args.get("action_repeat", str(action_repeat)).to_int()
func disconnect_from_server():
stream.disconnect_from_host()
func handle_message() -> bool:
# get json message: reset, step, close
var message = _get_dict_json_message()
if message["type"] == "close":
print("received close message, closing game")
get_tree().quit()
get_tree().set_pause(false)
return true
if message["type"] == "reset":
print("resetting all agents")
_reset_all_agents()
just_reset = true
get_tree().set_pause(false)
#print("resetting forcing draw")
# RenderingServer.force_draw()
# var obs = _get_obs_from_agents()
# print("obs ", obs)
# var reply = {
# "type": "reset",
# "obs": obs
# }
# _send_dict_as_json_message(reply)
return true
if message["type"] == "call":
var method = message["method"]
var returns = _call_method_on_agents(method)
var reply = {
"type": "call",
"returns": returns
}
print("calling method from Python")
_send_dict_as_json_message(reply)
return handle_message()
if message["type"] == "action":
var action = message["action"]
_set_agent_actions(action)
need_to_send_obs = true
get_tree().set_pause(false)
return true
print("message was not handled")
return false
func _call_method_on_agents(method):
var returns = []
for agent in agents:
returns.append(agent.call(method))
return returns
func _reset_agents_if_done():
for agent in agents:
if agent.get_done():
agent.set_done_false()
func _reset_all_agents():
for agent in agents:
agent.needs_reset = true
#agent.reset()
func _get_obs_from_agents():
var obs = []
for agent in agents:
obs.append(agent.get_obs())
return obs
func _get_reward_from_agents():
var rewards = []
for agent in agents:
rewards.append(agent.get_reward())
agent.zero_reward()
return rewards
func _get_done_from_agents():
var dones = []
for agent in agents:
var done = agent.get_done()
if done: agent.set_done_false()
dones.append(done)
return dones
func _set_agent_actions(actions):
for i in range(len(actions)):
agents[i].set_action(actions[i])
func clamp_array(arr : Array, min:float, max:float):
var output : Array = []
for a in arr:
output.append(clamp(a, min, max))
return output
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