Update GODOT part

addons/godot_rl_agents/controller/ai_controller_2d.gd

@@ -34,37 +34,37 @@ var _player: Node2D
 
 
 func _ready():
-    add_to_group("AGENT")
+	add_to_group("AGENT")
 
 
 func init(player: Node2D):
-    _player = player
+	_player = player
 
 
 #-- Methods that need implementing using the "extend script" option in Godot --#
 func get_obs() -> Dictionary:
-    assert(false, "the get_obs method is not implemented when extending from ai_controller")
-    return {"obs": []}
+	assert(false, "the get_obs method is not implemented when extending from ai_controller")
+	return {"obs": []}
 
 
 func get_reward() -> float:
-    assert(false, "the get_reward method is not implemented when extending from ai_controller")
-    return 0.0
+	assert(false, "the get_reward method is not implemented when extending from ai_controller")
+	return 0.0
 
 
 func get_action_space() -> Dictionary:
-    assert(
-        false,
-        "the get get_action_space method is not implemented when extending from ai_controller"
-    )
-    return {
-        "example_actions_continous": {"size": 2, "action_type": "continuous"},
-        "example_actions_discrete": {"size": 2, "action_type": "discrete"},
-    }
+	assert(
+		false,
+		"the get get_action_space method is not implemented when extending from ai_controller"
+	)
+	return {
+		"example_actions_continous": {"size": 2, "action_type": "continuous"},
+		"example_actions_discrete": {"size": 2, "action_type": "discrete"},
+	}
 
 
 func set_action(action) -> void:
-    assert(false, "the set_action method is not implemented when extending from ai_controller")
+	assert(false, "the set_action method is not implemented when extending from ai_controller")
 
 
 #-----------------------------------------------------------------------------#
@@ -73,47 +73,47 @@ func set_action(action) -> void:
 #-- Methods that sometimes need implementing using the "extend script" option in Godot --#
 # Only needed if you are recording expert demos with this AIController
 func get_action() -> Array:
-    assert(false, "the get_action method is not implemented in extended AIController but demo_recorder is used")
-    return []
+	assert(false, "the get_action method is not implemented in extended AIController but demo_recorder is used")
+	return []
 
 # -----------------------------------------------------------------------------#
 
 func _physics_process(delta):
-    n_steps += 1
-    if n_steps > reset_after:
-        needs_reset = true
+	n_steps += 1
+	if n_steps > reset_after:
+		needs_reset = true
 
 
 func get_obs_space():
-    # may need overriding if the obs space is complex
-    var obs = get_obs()
-    return {
-        "obs": {"size": [len(obs["obs"])], "space": "box"},
-    }
+	# may need overriding if the obs space is complex
+	var obs = get_obs()
+	return {
+		"obs": {"size": [len(obs["obs"])], "space": "box"},
+	}
 
 
 func reset():
-    n_steps = 0
-    needs_reset = false
+	n_steps = 0
+	needs_reset = false
 
 
 func reset_if_done():
-    if done:
-        reset()
+	if done:
+		reset()
 
 
 func set_heuristic(h):
-    # sets the heuristic from "human" or "model" nothing to change here
-    heuristic = h
+	# sets the heuristic from "human" or "model" nothing to change here
+	heuristic = h
 
 
 func get_done():
-    return done
+	return done
 
 
 func set_done_false():
-    done = false
+	done = false
 
 
 func zero_reward():
-    reward = 0.0
+	reward = 0.0

addons/godot_rl_agents/sync.gd

@@ -51,430 +51,430 @@ var _obs_space_training: Array[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)
+	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()
-    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
-    )
+	_get_agents()
+	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
+	)
 
-    _set_heuristic("human", all_agents)
+	_set_heuristic("human", all_agents)
 
-    _initialize_training_agents()
-    _initialize_inference_agents()
-    _initialize_demo_recording()
+	_initialize_training_agents()
+	_initialize_inference_agents()
+	_initialize_demo_recording()
 
-    _set_seed()
-    _set_action_repeat()
-    initialized = true
+	_set_seed()
+	_set_action_repeat()
+	initialized = true
 
 
 func _initialize_training_agents():
-    if agents_training.size() > 0:
-        _obs_space_training.resize(agents_training.size())
-        _action_space_training.resize(agents_training.size())
-        for agent_idx in range(0, agents_training.size()):
-            _obs_space_training[agent_idx] = agents_training[agent_idx].get_obs_space()
-            _action_space_training[agent_idx] = agents_training[agent_idx].get_action_space()
-        connected = connect_to_server()
-        if connected:
-            _set_heuristic("model", agents_training)
-            _handshake()
-            _send_env_info()
-        else:
-            push_warning(
-                "Couldn't connect to Python server, using human controls instead. ",
-                "Did you start the training server using e.g. `gdrl` from the console?"
-            )
+	if agents_training.size() > 0:
+		_obs_space_training.resize(agents_training.size())
+		_action_space_training.resize(agents_training.size())
+		for agent_idx in range(0, agents_training.size()):
+			_obs_space_training[agent_idx] = agents_training[agent_idx].get_obs_space()
+			_action_space_training[agent_idx] = agents_training[agent_idx].get_action_space()
+		connected = connect_to_server()
+		if connected:
+			_set_heuristic("model", agents_training)
+			_handshake()
+			_send_env_info()
+		else:
+			push_warning(
+				"Couldn't connect to Python server, using human controls instead. ",
+				"Did you start the training server using e.g. `gdrl` from the console?"
+			)
 
 
 func _initialize_inference_agents():
-    if agents_inference.size() > 0:
-        if control_mode == ControlModes.ONNX_INFERENCE:
-            assert(
-                FileAccess.file_exists(onnx_model_path),
-                "Onnx Model Path set on Sync node does not exist: %s" % onnx_model_path
-            )
-            onnx_models[onnx_model_path] = ONNXModel.new(onnx_model_path, 1)
+	if agents_inference.size() > 0:
+		if control_mode == ControlModes.ONNX_INFERENCE:
+			assert(
+				FileAccess.file_exists(onnx_model_path),
+				"Onnx Model Path set on Sync node does not exist: %s" % onnx_model_path
+			)
+			onnx_models[onnx_model_path] = ONNXModel.new(onnx_model_path, 1)
 
-        for agent in agents_inference:
-            var action_space = agent.get_action_space()
-            _action_space_inference.append(action_space)
+		for agent in agents_inference:
+			var action_space = agent.get_action_space()
+			_action_space_inference.append(action_space)
 
-            var agent_onnx_model: ONNXModel
-            if agent.onnx_model_path.is_empty():
-                assert(
-                    onnx_models.has(onnx_model_path),
-                    (
-                        "Node %s has no onnx model path set " % agent.get_path()
-                        + "and sync node's control mode is not set to OnnxInference. "
-                        + "Either add the path to the AIController, "
-                        + "or if you want to use the path set on sync node instead, "
-                        + "set control mode to OnnxInference."
-                    )
-                )
-                prints(
-                    "Info: AIController %s" % agent.get_path(),
-                    "has no onnx model path set.",
-                    "Using path set on the sync node instead."
-                )
-                agent_onnx_model = onnx_models[onnx_model_path]
-            else:
-                if not onnx_models.has(agent.onnx_model_path):
-                    assert(
-                        FileAccess.file_exists(agent.onnx_model_path),
-                        (
-                            "Onnx Model Path set on %s node does not exist: %s"
-                            % [agent.get_path(), agent.onnx_model_path]
-                        )
-                    )
-                    onnx_models[agent.onnx_model_path] = ONNXModel.new(agent.onnx_model_path, 1)
-                agent_onnx_model = onnx_models[agent.onnx_model_path]
+			var agent_onnx_model: ONNXModel
+			if agent.onnx_model_path.is_empty():
+				assert(
+					onnx_models.has(onnx_model_path),
+					(
+						"Node %s has no onnx model path set " % agent.get_path()
+						+ "and sync node's control mode is not set to OnnxInference. "
+						+ "Either add the path to the AIController, "
+						+ "or if you want to use the path set on sync node instead, "
+						+ "set control mode to OnnxInference."
+					)
+				)
+				prints(
+					"Info: AIController %s" % agent.get_path(),
+					"has no onnx model path set.",
+					"Using path set on the sync node instead."
+				)
+				agent_onnx_model = onnx_models[onnx_model_path]
+			else:
+				if not onnx_models.has(agent.onnx_model_path):
+					assert(
+						FileAccess.file_exists(agent.onnx_model_path),
+						(
+							"Onnx Model Path set on %s node does not exist: %s"
+							% [agent.get_path(), agent.onnx_model_path]
+						)
+					)
+					onnx_models[agent.onnx_model_path] = ONNXModel.new(agent.onnx_model_path, 1)
+				agent_onnx_model = onnx_models[agent.onnx_model_path]
 
-            agent.onnx_model = agent_onnx_model
-            if not agent_onnx_model.action_means_only_set:
-                agent_onnx_model.set_action_means_only(action_space)
+			agent.onnx_model = agent_onnx_model
+			if not agent_onnx_model.action_means_only_set:
+				agent_onnx_model.set_action_means_only(action_space)
 				
-        _set_heuristic("model", agents_inference)
+		_set_heuristic("model", agents_inference)
 
 
 func _initialize_demo_recording():
-    if agent_demo_record:
-        expert_demo_save_path = agent_demo_record.expert_demo_save_path
-        assert(
-            not expert_demo_save_path.is_empty(),
-            "Expert demo save path set in %s is empty." % agent_demo_record.get_path()
-        )
+	if agent_demo_record:
+		expert_demo_save_path = agent_demo_record.expert_demo_save_path
+		assert(
+			not expert_demo_save_path.is_empty(),
+			"Expert demo save path set in %s is empty." % agent_demo_record.get_path()
+		)
 
-        InputMap.add_action("RemoveLastDemoEpisode")
-        InputMap.action_add_event(
-            "RemoveLastDemoEpisode", agent_demo_record.remove_last_episode_key
-        )
-        current_demo_trajectory.resize(2)
-        current_demo_trajectory[0] = []
-        current_demo_trajectory[1] = []
-        agent_demo_record.heuristic = "demo_record"
+		InputMap.add_action("RemoveLastDemoEpisode")
+		InputMap.action_add_event(
+			"RemoveLastDemoEpisode", agent_demo_record.remove_last_episode_key
+		)
+		current_demo_trajectory.resize(2)
+		current_demo_trajectory[0] = []
+		current_demo_trajectory[1] = []
+		agent_demo_record.heuristic = "demo_record"
 
 
 func _physics_process(_delta):
-    # two modes, human control, agent control
-    # pause tree, send obs, get actions, set actions, unpause tree
+	# two modes, human control, agent control
+	# pause tree, send obs, get actions, set actions, unpause tree
 
-    _demo_record_process()
+	_demo_record_process()
 
-    if n_action_steps % action_repeat != 0:
-        n_action_steps += 1
-        return
+	if n_action_steps % action_repeat != 0:
+		n_action_steps += 1
+		return
 
-    n_action_steps += 1
+	n_action_steps += 1
 
-    _training_process()
-    _inference_process()
-    _heuristic_process()
+	_training_process()
+	_inference_process()
+	_heuristic_process()
 
 
 func _training_process():
-    if connected:
-        get_tree().set_pause(true)
+	if connected:
+		get_tree().set_pause(true)
 
-        if just_reset:
-            just_reset = false
-            var obs = _get_obs_from_agents(agents_training)
+		if just_reset:
+			just_reset = false
+			var obs = _get_obs_from_agents(agents_training)
 
-            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
+			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
+		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(agents_training)
+			var obs = _get_obs_from_agents(agents_training)
 
-            var reply = {"type": "step", "obs": obs, "reward": reward, "done": done}
-            _send_dict_as_json_message(reply)
+			var reply = {"type": "step", "obs": obs, "reward": reward, "done": done}
+			_send_dict_as_json_message(reply)
 
-        var handled = handle_message()
+		var handled = handle_message()
 
 
 func _inference_process():
-    if agents_inference.size() > 0:
-        var obs: Array = _get_obs_from_agents(agents_inference)
-        var actions = []
+	if agents_inference.size() > 0:
+		var obs: Array = _get_obs_from_agents(agents_inference)
+		var actions = []
 
-        for agent_id in range(0, agents_inference.size()):
-            var model: ONNXModel = agents_inference[agent_id].onnx_model
-            var action = model.run_inference(
-                obs[agent_id]["obs"], 1.0
-            )
-            var action_dict = _extract_action_dict(
-                action["output"], _action_space_inference[agent_id], model.action_means_only
-            )
-            actions.append(action_dict)
+		for agent_id in range(0, agents_inference.size()):
+			var model: ONNXModel = agents_inference[agent_id].onnx_model
+			var action = model.run_inference(
+				obs[agent_id]["obs"], 1.0
+			)
+			var action_dict = _extract_action_dict(
+				action["output"], _action_space_inference[agent_id], model.action_means_only
+			)
+			actions.append(action_dict)
 
-        _set_agent_actions(actions, agents_inference)
-        _reset_agents_if_done(agents_inference)
-        get_tree().set_pause(false)
+		_set_agent_actions(actions, agents_inference)
+		_reset_agents_if_done(agents_inference)
+		get_tree().set_pause(false)
 
 
 func _demo_record_process():
-    if not agent_demo_record:
-        return
+	if not agent_demo_record:
+		return
 
-    if Input.is_action_just_pressed("RemoveLastDemoEpisode"):
-        print("[Sync script][Demo recorder] Removing last recorded episode.")
-        demo_trajectories.remove_at(demo_trajectories.size() - 1)
-        print("Remaining episode count: %d" % demo_trajectories.size())
+	if Input.is_action_just_pressed("RemoveLastDemoEpisode"):
+		print("[Sync script][Demo recorder] Removing last recorded episode.")
+		demo_trajectories.remove_at(demo_trajectories.size() - 1)
+		print("Remaining episode count: %d" % demo_trajectories.size())
 
-    if n_action_steps % agent_demo_record.action_repeat != 0:
-        return
+	if n_action_steps % agent_demo_record.action_repeat != 0:
+		return
 
-    var obs_dict: Dictionary = agent_demo_record.get_obs()
+	var obs_dict: Dictionary = agent_demo_record.get_obs()
 
-    # Get the current obs from the agent
-    assert(
-        obs_dict.has("obs"),
-        "Demo recorder needs an 'obs' key in get_obs() returned dictionary to record obs from."
-    )
-    current_demo_trajectory[0].append(obs_dict.obs)
+	# Get the current obs from the agent
+	assert(
+		obs_dict.has("obs"),
+		"Demo recorder needs an 'obs' key in get_obs() returned dictionary to record obs from."
+	)
+	current_demo_trajectory[0].append(obs_dict.obs)
 
-    # Get the action applied for the current obs from the agent
-    agent_demo_record.set_action()
-    var acts = agent_demo_record.get_action()
+	# Get the action applied for the current obs from the agent
+	agent_demo_record.set_action()
+	var acts = agent_demo_record.get_action()
 
-    var terminal = agent_demo_record.get_done()
-    # Record actions only for non-terminal states
-    if terminal:
-        agent_demo_record.set_done_false()
-    else:
-        current_demo_trajectory[1].append(acts)
+	var terminal = agent_demo_record.get_done()
+	# Record actions only for non-terminal states
+	if terminal:
+		agent_demo_record.set_done_false()
+	else:
+		current_demo_trajectory[1].append(acts)
 
-    if terminal:
-        #current_demo_trajectory[2].append(true)
-        demo_trajectories.append(current_demo_trajectory.duplicate(true))
-        print("[Sync script][Demo recorder] Recorded episode count: %d" % demo_trajectories.size())
-        current_demo_trajectory[0].clear()
-        current_demo_trajectory[1].clear()
+	if terminal:
+		#current_demo_trajectory[2].append(true)
+		demo_trajectories.append(current_demo_trajectory.duplicate(true))
+		print("[Sync script][Demo recorder] Recorded episode count: %d" % demo_trajectories.size())
+		current_demo_trajectory[0].clear()
+		current_demo_trajectory[1].clear()
 
 
 func _heuristic_process():
-    for agent in agents_heuristic:
-        _reset_agents_if_done(agents_heuristic)
+	for agent in agents_heuristic:
+		_reset_agents_if_done(agents_heuristic)
 
 
 func _extract_action_dict(action_array: Array, action_space: Dictionary, action_means_only: bool):
-    var index = 0
-    var result = {}
-    for key in action_space.keys():	
-        var size = action_space[key]["size"]	
-        var action_type = action_space[key]["action_type"]
-        if action_type == "discrete":
-            var largest_logit: float # Value of the largest logit for this action in the actions array
-            var largest_logit_idx: int # Index of the largest logit for this action in the actions array
-            for logit_idx in range(0, size):
-                var logit_value = action_array[index + logit_idx]
-                if logit_value > largest_logit:
-                    largest_logit = logit_value
-                    largest_logit_idx = logit_idx 
-            result[key] = largest_logit_idx # Index of the largest logit is the discrete action value
-            index += size
-        elif action_type == "continuous":
-            # For continous actions, we only take the action mean values
-            result[key] = clamp_array(action_array.slice(index, index + size), -1.0, 1.0)
-            if action_means_only:
-                index += size # model only outputs action means, so we move index by size
-            else:
-                index += size * 2 # model outputs logstd after action mean, we skip the logstd part
+	var index = 0
+	var result = {}
+	for key in action_space.keys():	
+		var size = action_space[key]["size"]	
+		var action_type = action_space[key]["action_type"]
+		if action_type == "discrete":
+			var largest_logit: float # Value of the largest logit for this action in the actions array
+			var largest_logit_idx: int # Index of the largest logit for this action in the actions array
+			for logit_idx in range(0, size):
+				var logit_value = action_array[index + logit_idx]
+				if logit_value > largest_logit:
+					largest_logit = logit_value
+					largest_logit_idx = logit_idx 
+			result[key] = largest_logit_idx # Index of the largest logit is the discrete action value
+			index += size
+		elif action_type == "continuous":
+			# For continous actions, we only take the action mean values
+			result[key] = clamp_array(action_array.slice(index, index + size), -1.0, 1.0)
+			if action_means_only:
+				index += size # model only outputs action means, so we move index by size
+			else:
+				index += size * 2 # model outputs logstd after action mean, we skip the logstd part
 
-        else:
-            assert(false, 'Only "discrete" and "continuous" action types supported. Found: %s action type set.' % action_type)
+		else:
+			assert(false, 'Only "discrete" and "continuous" action types supported. Found: %s action type set.' % action_type)
 		
 
-    return result
+	return result
 
 
 ## For AIControllers that inherit mode from sync, sets the correct mode.
 func _set_agent_mode(agent: Node):
-    var agent_inherits_mode: bool = agent.control_mode == agent.ControlModes.INHERIT_FROM_SYNC
+	var agent_inherits_mode: bool = agent.control_mode == agent.ControlModes.INHERIT_FROM_SYNC
 
-    if agent_inherits_mode:
-        match control_mode:
-            ControlModes.HUMAN:
-                agent.control_mode = agent.ControlModes.HUMAN
-            ControlModes.TRAINING:
-                agent.control_mode = agent.ControlModes.TRAINING
-            ControlModes.ONNX_INFERENCE:
-                agent.control_mode = agent.ControlModes.ONNX_INFERENCE
+	if agent_inherits_mode:
+		match control_mode:
+			ControlModes.HUMAN:
+				agent.control_mode = agent.ControlModes.HUMAN
+			ControlModes.TRAINING:
+				agent.control_mode = agent.ControlModes.TRAINING
+			ControlModes.ONNX_INFERENCE:
+				agent.control_mode = agent.ControlModes.ONNX_INFERENCE
 
 
 func _get_agents():
-    all_agents = get_tree().get_nodes_in_group("AGENT")
-    for agent in all_agents:
-        _set_agent_mode(agent)
+	all_agents = get_tree().get_nodes_in_group("AGENT")
+	for agent in all_agents:
+		_set_agent_mode(agent)
 
-        if agent.control_mode == agent.ControlModes.TRAINING:
-            agents_training.append(agent)
-        elif agent.control_mode == agent.ControlModes.ONNX_INFERENCE:
-            agents_inference.append(agent)
-        elif agent.control_mode == agent.ControlModes.HUMAN:
-            agents_heuristic.append(agent)
-        elif agent.control_mode == agent.ControlModes.RECORD_EXPERT_DEMOS:
-            assert(
-                not agent_demo_record,
-                "Currently only a single AIController can be used for recording expert demos."
-            )
-            agent_demo_record = agent
+		if agent.control_mode == agent.ControlModes.TRAINING:
+			agents_training.append(agent)
+		elif agent.control_mode == agent.ControlModes.ONNX_INFERENCE:
+			agents_inference.append(agent)
+		elif agent.control_mode == agent.ControlModes.HUMAN:
+			agents_heuristic.append(agent)
+		elif agent.control_mode == agent.ControlModes.RECORD_EXPERT_DEMOS:
+			assert(
+				not agent_demo_record,
+				"Currently only a single AIController can be used for recording expert demos."
+			)
+			agent_demo_record = agent
 	
-    var training_agent_count = agents_training.size()
-    agents_training_policy_names.resize(training_agent_count)
-    for i in range(0, training_agent_count):
-        agents_training_policy_names[i] = agents_training[i].policy_name
+	var training_agent_count = agents_training.size()
+	agents_training_policy_names.resize(training_agent_count)
+	for i in range(0, training_agent_count):
+		agents_training_policy_names[i] = agents_training[i].policy_name
 
 
 func _set_heuristic(heuristic, agents: Array):
-    for agent in agents:
-        agent.set_heuristic(heuristic)
+	for agent in agents:
+		agent.set_heuristic(heuristic)
 
 
 func _handshake():
-    print("performing 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)
+	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")
+	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
+	# 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)
+		OS.delay_usec(10)
 
-    var message = stream.get_string()
-    var json_data = JSON.parse_string(message)
+	var message = stream.get_string()
+	var json_data = JSON.parse_string(message)
 
-    return json_data
+	return json_data
 
 
 func _send_dict_as_json_message(dict):
-    stream.put_string(JSON.stringify(dict, "", false))
+	stream.put_string(JSON.stringify(dict, "", false))
 
 
 func _send_env_info():
-    var json_dict = _get_dict_json_message()
-    assert(json_dict["type"] == "env_info")
+	var json_dict = _get_dict_json_message()
+	assert(json_dict["type"] == "env_info")
 
-    var message = {
-        "type": "env_info",
-        "observation_space": _obs_space_training,
-        "action_space": _action_space_training,
-        "n_agents": len(agents_training),
-        "agent_policy_names": agents_training_policy_names
-    }
-    _send_dict_as_json_message(message)
+	var message = {
+		"type": "env_info",
+		"observation_space": _obs_space_training,
+		"action_space": _action_space_training,
+		"n_agents": len(agents_training),
+		"agent_policy_names": agents_training_policy_names
+	}
+	_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()
+	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
+	# "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("--")] = ""
+	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
+	return arguments
 
 
 func _get_speedup():
-    print(args)
-    return args.get("speedup", str(speed_up)).to_float()
+	print(args)
+	return args.get("speedup", str(speed_up)).to_float()
 
 
 func _get_port():
-    return args.get("port", DEFAULT_PORT).to_int()
+	return args.get("port", DEFAULT_PORT).to_int()
 
 
 func _set_seed():
-    var _seed = args.get("env_seed", DEFAULT_SEED).to_int()
-    seed(_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()
+	action_repeat = args.get("action_repeat", str(action_repeat)).to_int()
 
 
 func disconnect_from_server():
-    stream.disconnect_from_host()
+	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
+	# 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_agents()
-        just_reset = true
-        get_tree().set_pause(false)
-        #print("resetting forcing draw")
+	if message["type"] == "reset":
+		print("resetting all agents")
+		_reset_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)
@@ -483,97 +483,97 @@ func handle_message() -> bool:
 #            "obs": obs
 #        }
 #        _send_dict_as_json_message(reply)
-        return true
+		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"] == "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, agents_training)
-        need_to_send_obs = true
-        get_tree().set_pause(false)
-        return true
+	if message["type"] == "action":
+		var action = message["action"]
+		_set_agent_actions(action, agents_training)
+		need_to_send_obs = true
+		get_tree().set_pause(false)
+		return true
 
-    print("message was not handled")
-    return false
+	print("message was not handled")
+	return false
 
 
 func _call_method_on_agents(method):
-    var returns = []
-    for agent in all_agents:
-        returns.append(agent.call(method))
+	var returns = []
+	for agent in all_agents:
+		returns.append(agent.call(method))
 
-    return returns
+	return returns
 
 
 func _reset_agents_if_done(agents = all_agents):
-    for agent in agents:
-        if agent.get_done():
-            agent.set_done_false()
+	for agent in agents:
+		if agent.get_done():
+			agent.set_done_false()
 
 
 func _reset_agents(agents = all_agents):
-    for agent in agents:
-        agent.needs_reset = true
-        #agent.reset()
+	for agent in agents:
+		agent.needs_reset = true
+		#agent.reset()
 
 
 func _get_obs_from_agents(agents: Array = all_agents):
-    var obs = []
-    for agent in agents:
-        obs.append(agent.get_obs())
-    return obs
+	var obs = []
+	for agent in agents:
+		obs.append(agent.get_obs())
+	return obs
 
 
 func _get_reward_from_agents(agents: Array = agents_training):
-    var rewards = []
-    for agent in agents:
-        rewards.append(agent.get_reward())
-        agent.zero_reward()
-    return rewards
+	var rewards = []
+	for agent in agents:
+		rewards.append(agent.get_reward())
+		agent.zero_reward()
+	return rewards
 
 
 func _get_done_from_agents(agents: Array = agents_training):
-    var dones = []
-    for agent in agents:
-        var done = agent.get_done()
-        if done:
-            agent.set_done_false()
-        dones.append(done)
-    return dones
+	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, agents: Array = all_agents):
-    for i in range(len(actions)):
-        agents[i].set_action(actions[i])
+	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
+	var output: Array = []
+	for a in arr:
+		output.append(clamp(a, min, max))
+	return output
 
 
 ## Save recorded export demos on window exit (Close game window instead of "Stop" button in Godot Editor)
 func _notification(what):
-    if demo_trajectories.size() == 0 or expert_demo_save_path.is_empty():
-        return
+	if demo_trajectories.size() == 0 or expert_demo_save_path.is_empty():
+		return
 
-    if what == NOTIFICATION_PREDELETE:
-        var json_string = JSON.stringify(demo_trajectories, "", false)
-        var file = FileAccess.open(expert_demo_save_path, FileAccess.WRITE)
+	if what == NOTIFICATION_PREDELETE:
+		var json_string = JSON.stringify(demo_trajectories, "", false)
+		var file = FileAccess.open(expert_demo_save_path, FileAccess.WRITE)
 
-        if not file:
-            var error: Error = FileAccess.get_open_error()
-            assert(not error, "There was an error opening the file: %d" % error)
+		if not file:
+			var error: Error = FileAccess.get_open_error()
+			assert(not error, "There was an error opening the file: %d" % error)
 
-        file.store_line(json_string)
-        var error = file.get_error()
-        assert(not error, "There was an error after trying to write to the file: %d" % error)
+		file.store_line(json_string)
+		var error = file.get_error()
+		assert(not error, "There was an error after trying to write to the file: %d" % error)