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