Merge remote-tracking branch 'origin/master'

.gitignore

@@ -0,0 +1,18 @@
+.venv/
+.vscode/
+**/__pycache__/
+poetry.lock
+poetry.toml
+**/*.log
+**/*.txt
+**/build/
+**/install/
+**/log/
+*.spec
+dist/
+*.zip
+*.csv
+*.json
+*.xml
+*.npz
+*.pkl

communication/server.py

@@ -1,5 +1,7 @@
 import logging
+import os
 import socket
+import time
 from select import select
 from communication.world_parser import WorldParser
 
@@ -10,15 +12,32 @@ class Server:
     def __init__(self, host: str, port: int, world_parser: WorldParser):
         self.world_parser: WorldParser = world_parser
         self.__host: str = host
-        self.__port: str = port
+        self.__port: int = port
-        self.__socket: socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        self.__socket: socket.socket = self._create_socket()
-        self.__socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
         self.__send_buff = []
         self.__rcv_buffer_size = 1024
+        self.__rcv_buffer_default_size = 1024
+        self.__max_msg_size = 1048576
+        self.__shrink_threshold = 8192
+        self.__shrink_after_msgs = 200
+        self.__small_msg_streak = 0
         self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
 
+    def _create_socket(self) -> socket.socket:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
+        return sock
+
     def connect(self) -> None:
         logger.info("Connecting to server at %s:%d...", self.__host, self.__port)
+
+        # Always reconnect with a fresh socket object.
+        try:
+            self.__socket.close()
+        except OSError:
+            pass
+        self.__socket = self._create_socket()
+
         while True:
             try:
                 self.__socket.connect((self.__host, self.__port))
@@ -27,12 +46,19 @@ class Server:
                 logger.error(
                     "Connection refused. Make sure the server is running and listening on {self.__host}:{self.__port}."
                 )
+                time.sleep(0.05)
 
         logger.info(f"Server connection established to {self.__host}:{self.__port}.")
 
     def shutdown(self) -> None:
-        self.__socket.close()
+        try:
             self.__socket.shutdown(socket.SHUT_RDWR)
+        except OSError:
+            pass
+        try:
+            self.__socket.close()
+        except OSError:
+            pass
 
     def send_immediate(self, msg: str) -> None:
         """
@@ -50,10 +76,13 @@ class Server:
         """
         Send all committed messages
         """
-        if len(select([self.__socket], [], [], 0.0)[0]) == 0:
+        if not self.__send_buff:
+            return
+
+        if len(select([self.__socket], [], [], 0.0)[0]) != 0:
+            logger.debug("Socket is readable while sending; keeping full-duplex command send.")
+
         self.send_immediate(("".join(self.__send_buff)))
-        else:
-            logger.info("Server_Comm.py: Received a new packet while thinking!")
         self.__send_buff = []
 
     def commit(self, msg: str) -> None:
@@ -69,37 +98,50 @@ class Server:
         self.commit(msg)
         self.send()
 
-    def receive(self) -> None:
+    def receive(self):
-        """
+
-        Receive the next message from the TCP/IP socket and updates world
+        while True:
-        """
 
-        # Receive message length information
             if (
                     self.__socket.recv_into(
                         self.__rcv_buffer, nbytes=4, flags=socket.MSG_WAITALL
-            )
+                    ) != 4
-            != 4
             ):
                 raise ConnectionResetError
 
             msg_size = int.from_bytes(self.__rcv_buffer[:4], byteorder="big", signed=False)
 
-        # Ensure receive buffer is large enough to hold the message
+            # Guard against corrupted frame lengths that would trigger huge allocations.
+            if msg_size <= 0 or msg_size > self.__max_msg_size:
+                raise ConnectionResetError
+
             if msg_size > self.__rcv_buffer_size:
                 self.__rcv_buffer_size = msg_size
                 self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
 
-        # Receive message with the specified length
             if (
                     self.__socket.recv_into(
                         self.__rcv_buffer, nbytes=msg_size, flags=socket.MSG_WAITALL
-            )
+                    ) != msg_size
-            != msg_size
             ):
                 raise ConnectionResetError
 
-        self.world_parser.parse(message=self.__rcv_buffer[:msg_size].decode())
+            self.world_parser.parse(
+                message=self.__rcv_buffer[:msg_size].decode()
+            )
+
+            if msg_size <= self.__shrink_threshold and self.__rcv_buffer_size > self.__rcv_buffer_default_size:
+                self.__small_msg_streak += 1
+                if self.__small_msg_streak >= self.__shrink_after_msgs:
+                    self.__rcv_buffer_size = self.__rcv_buffer_default_size
+                    self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
+                    self.__small_msg_streak = 0
+            else:
+                self.__small_msg_streak = 0
+
+            # 如果socket没有更多数据就退出
+            if len(select([self.__socket], [], [], 0.0)[0]) == 0:
+                break
 
     def commit_beam(self, pos2d: list, rotation: float) -> None:
         assert len(pos2d) == 2

communication/world_parser.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import re
 import numpy as np
 from scipy.spatial.transform import Rotation as R
@@ -7,6 +8,16 @@ from utils.math_ops import MathOps
 from world.commons.play_mode import PlayModeEnum
 
 logger = logging.getLogger()
+DEBUG_LOG_FILE = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
+
+
+def _debug_log(message: str) -> None:
+    print(message)
+    try:
+        with open(DEBUG_LOG_FILE, "a", encoding="utf-8") as f:
+            f.write(message + "\n")
+    except OSError:
+        pass
 
 
 class WorldParser:
@@ -14,6 +25,36 @@ class WorldParser:
         from agent.base_agent import Base_Agent  # type hinting
 
         self.agent: Base_Agent = agent
+        self._hj_debug_prints = 0
+
+    def _normalize_motor_name(self, motor_name: str) -> str:
+        alias_map = {
+            "q_hj1": "he1",
+            "q_hj2": "he2",
+            "q_laj1": "lae1",
+            "q_laj2": "lae2",
+            "q_laj3": "lae3",
+            "q_laj4": "lae4",
+            "q_raj1": "rae1",
+            "q_raj2": "rae2",
+            "q_raj3": "rae3",
+            "q_raj4": "rae4",
+            "q_wj1": "te1",
+            "q_tj1": "te1",
+            "q_llj1": "lle1",
+            "q_llj2": "lle2",
+            "q_llj3": "lle3",
+            "q_llj4": "lle4",
+            "q_llj5": "lle5",
+            "q_llj6": "lle6",
+            "q_rlj1": "rle1",
+            "q_rlj2": "rle2",
+            "q_rlj3": "rle3",
+            "q_rlj4": "rle4",
+            "q_rlj5": "rle5",
+            "q_rlj6": "rle6",
+        }
+        return alias_map.get(motor_name, motor_name)
 
     def parse(self, message: str) -> None:
         perception_dict: dict = self.__sexpression_to_dict(message)
@@ -51,9 +92,29 @@ class WorldParser:
 
         robot = self.agent.robot
 
-        robot.motor_positions = {h["n"]: h["ax"] for h in perception_dict["HJ"]}
+        hj_states = perception_dict["HJ"] if isinstance(perception_dict["HJ"], list) else [perception_dict["HJ"]]
 
-        robot.motor_speeds = {h["n"]: h["vx"] for h in perception_dict["HJ"]}
+        if self._hj_debug_prints < 5:
+            names = [joint_state.get("n", "<missing>") for joint_state in hj_states]
+            normalized_names = [self._normalize_motor_name(name) for name in names]
+            matched_names = [name for name in names if name in robot.motor_positions]
+            matched_normalized_names = [name for name in normalized_names if name in robot.motor_positions]
+            # _debug_log(
+            #     "[ParserDebug] "
+            #     f"hj_count={len(hj_states)} "
+            #     f"sample_names={names[:8]} "
+            #     f"normalized_sample={normalized_names[:8]} "
+            #     f"matched={len(matched_names)}/{len(names)} "
+            #     f"matched_normalized={len(matched_normalized_names)}/{len(normalized_names)}"
+            # )
+            self._hj_debug_prints += 1
+
+        for joint_state in hj_states:
+            motor_name = self._normalize_motor_name(joint_state["n"])
+            if motor_name in robot.motor_positions:
+                robot.motor_positions[motor_name] = joint_state["ax"]
+            if motor_name in robot.motor_speeds:
+                robot.motor_speeds[motor_name] = joint_state["vx"]
 
         world._global_cheat_position = np.array(perception_dict["pos"]["p"])
 

readme.md

@@ -74,6 +74,21 @@ poetry run ./build_binary.sh <team-name>
 
 Once binary generation is finished, the result will be inside the build folder, as ```<team-name>.tar.gz```
 
+### GYM
+
+To use the gym, you need to install the following dependencies:
+```bash
+pip install gymnasium
+pip install psutil
+pip install stable-baselines3
+```
+
+Then, you can run gym examples under the ```GYM_CPU``` folder:
+```bash
+python3 -m scripts.gyms.Walk # Run the Walk gym example
+                             # of course, you can run other gym examples
+```
+
 ### Authors and acknowledgment
 This project was developed and contributed by:
 - **Chenxi Liu**

scripts/commons/Server.py

@@ -0,0 +1,148 @@
+import subprocess
+import os
+import time
+import threading
+
+
+class Server():
+    WATCHDOG_ENABLED = True
+    WATCHDOG_INTERVAL_SEC = 30.0
+    WATCHDOG_RSS_MB_LIMIT = 2000.0
+
+    def __init__(self, first_server_p, first_monitor_p, n_servers, no_render=True, no_realtime=True) -> None:
+        try:
+            import psutil
+            self.check_running_servers(psutil, first_server_p, first_monitor_p, n_servers)
+        except ModuleNotFoundError:
+            print("Info: Cannot check if the server is already running, because the psutil module was not found")
+
+        self.first_server_p = first_server_p
+        self.n_servers = n_servers
+        self.rcss_processes = []
+        self._server_specs = []
+        self._watchdog_stop = threading.Event()
+        self._watchdog_lock = threading.Lock()
+        self._watchdog_thread = None
+        first_monitor_p = first_monitor_p + 100
+
+        # makes it easier to kill test servers without affecting train servers
+        cmd = "rcssservermj"
+        render_arg = "--no-render" if no_render else ""
+        realtime_arg = "--no-realtime" if no_realtime else ""
+        for i in range(n_servers):
+            port = first_server_p + i
+            mport = first_monitor_p + i
+            self._server_specs.append((port, mport, cmd, render_arg, realtime_arg))
+            proc = self._spawn_server(port, mport, cmd, render_arg, realtime_arg)
+            self.rcss_processes.append(proc)
+
+        if self.WATCHDOG_ENABLED:
+            self._watchdog_thread = threading.Thread(target=self._watchdog_loop, daemon=True)
+            self._watchdog_thread.start()
+
+    def _spawn_server(self, port, mport, cmd, render_arg, realtime_arg):
+        server_cmd = f"{cmd} -c {port} -m {mport} {render_arg} {realtime_arg}".strip()
+
+        proc = subprocess.Popen(
+            server_cmd.split(),
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.STDOUT,
+            start_new_session=True
+        )
+
+        # Avoid startup storm when launching many servers at once.
+        time.sleep(0.03)
+
+        rc = proc.poll()
+        if rc is not None:
+            raise RuntimeError(
+                f"rcssservermj exited early (code={rc}) on server port {port}, monitor port {mport}"
+            )
+
+        return proc
+
+    @staticmethod
+    def _pid_rss_mb(pid):
+        try:
+            with open(f"/proc/{pid}/status", "r", encoding="utf-8") as f:
+                for line in f:
+                    if line.startswith("VmRSS:"):
+                        parts = line.split()
+                        if len(parts) >= 2:
+                            # VmRSS is kB
+                            return float(parts[1]) / 1024.0
+        except (FileNotFoundError, ProcessLookupError, PermissionError, OSError):
+            return 0.0
+        return 0.0
+
+    def _restart_server_at_index(self, idx, reason):
+        port, mport, cmd, render_arg, realtime_arg = self._server_specs[idx]
+        old_proc = self.rcss_processes[idx]
+        try:
+            old_proc.terminate()
+            old_proc.wait(timeout=1.0)
+        except Exception:
+            try:
+                old_proc.kill()
+            except Exception:
+                pass
+
+        new_proc = self._spawn_server(port, mport, cmd, render_arg, realtime_arg)
+        self.rcss_processes[idx] = new_proc
+        print(
+            f"[ServerWatchdog] Restarted server idx={idx} port={port} monitor={mport} reason={reason}"
+        )
+
+    def _watchdog_loop(self):
+        while not self._watchdog_stop.wait(self.WATCHDOG_INTERVAL_SEC):
+            with self._watchdog_lock:
+                for i, proc in enumerate(self.rcss_processes):
+                    rc = proc.poll()
+                    if rc is not None:
+                        self._restart_server_at_index(i, f"exited:{rc}")
+                        continue
+
+                    rss_mb = self._pid_rss_mb(proc.pid)
+                    if rss_mb > self.WATCHDOG_RSS_MB_LIMIT:
+                        self._restart_server_at_index(i, f"rss_mb:{rss_mb:.1f}")
+
+    def check_running_servers(self, psutil, first_server_p, first_monitor_p, n_servers):
+        ''' Check if any server is running on chosen ports '''
+        found = False
+        p_list = [p for p in psutil.process_iter() if p.cmdline() and "rcssservermj" in " ".join(p.cmdline())]
+        range1 = (first_server_p, first_server_p + n_servers)
+        range2 = (first_monitor_p, first_monitor_p + n_servers)
+        bad_processes = []
+
+        for p in p_list:
+            # currently ignoring remaining default port when only one of the ports is specified (uncommon scenario)
+            ports = [int(arg) for arg in p.cmdline()[1:] if arg.isdigit()]
+            if len(ports) == 0:
+                ports = [60000, 60100]  # default server ports (changing this is unlikely)
+
+            conflicts = [str(port) for port in ports if (
+                    (range1[0] <= port < range1[1]) or (range2[0] <= port < range2[1]))]
+
+            if len(conflicts) > 0:
+                if not found:
+                    print("\nThere are already servers running on the same port(s)!")
+                    found = True
+                bad_processes.append(p)
+                print(f"Port(s) {','.join(conflicts)} already in use by \"{' '.join(p.cmdline())}\" (PID:{p.pid})")
+
+        if found:
+            print()
+            while True:
+                inp = input("Enter 'kill' to kill these processes or ctrl+c to abort. ")
+                if inp == "kill":
+                    for p in bad_processes:
+                        p.kill()
+                    return
+
+    def kill(self):
+        self._watchdog_stop.set()
+        if self._watchdog_thread is not None:
+            self._watchdog_thread.join(timeout=1.0)
+        for p in self.rcss_processes:
+            p.kill()
+        print(f"Killed {self.n_servers} rcssservermj processes starting at {self.first_server_p}")

scripts/commons/Train_Base.py

@@ -0,0 +1,601 @@
+from datetime import datetime, timedelta
+from itertools import count
+from os import listdir
+from os.path import isdir, join, isfile
+from scripts.commons.UI import UI
+from shutil import copy
+from stable_baselines3 import PPO
+from stable_baselines3.common.base_class import BaseAlgorithm
+from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback, CallbackList, BaseCallback
+from typing import Callable
+# from world.world import World
+from xml.dom import minidom
+import numpy as np
+import os, time, math, csv, select, sys
+import pickle
+import xml.etree.ElementTree as ET
+import shutil
+
+
+class Train_Base():
+    def __init__(self, script) -> None:
+        '''
+        When training with multiple environments (multiprocessing):
+            The server port is incremented as follows:
+                self.server_p, self.server_p+1, self.server_p+2, ...
+            We add +1000 to the initial monitor port, so than we can have more than 100 environments:
+                self.monitor_p+1000, self.monitor_p+1001, self.monitor_p+1002, ...
+        When testing we use self.server_p and self.monitor_p
+        '''
+
+        args = script.args
+        self.script = script
+        self.ip = args.i
+        self.server_p = args.p  # (initial) server port
+        self.monitor_p = args.m + 100  # monitor port when testing
+        self.monitor_p_1000 = args.m + 1100  # initial monitor port when training
+        self.robot_type = args.r
+        self.team = args.t
+        self.uniform = args.u
+        self.cf_last_time = 0
+        self.cf_delay = 0
+        # self.cf_target_period = World.STEPTIME # target simulation speed while testing (default: real-time)
+
+    @staticmethod
+    def prompt_user_for_model(self):
+
+        gyms_logs_path = "./scripts/gyms/logs/"
+        folders = [f for f in listdir(gyms_logs_path) if isdir(join(gyms_logs_path, f))]
+        folders.sort(key=lambda f: os.path.getmtime(join(gyms_logs_path, f)), reverse=True)  # sort by modification date
+
+        while True:
+            try:
+                folder_name = UI.print_list(folders, prompt="Choose folder (ctrl+c to return): ")[1]
+            except KeyboardInterrupt:
+                print()
+                return None  # ctrl+c
+
+            folder_dir = os.path.join(gyms_logs_path, folder_name)
+            models = [m[:-4] for m in listdir(folder_dir) if isfile(join(folder_dir, m)) and m.endswith(".zip")]
+
+            if not models:
+                print("The chosen folder does not contain any .zip file!")
+                continue
+
+            models.sort(key=lambda m: os.path.getmtime(join(folder_dir, m + ".zip")),
+                        reverse=True)  # sort by modification date
+
+            try:
+                model_name = UI.print_list(models, prompt="Choose model (ctrl+c to return): ")[1]
+                break
+            except KeyboardInterrupt:
+                print()
+
+        return {"folder_dir": folder_dir, "folder_name": folder_name,
+                "model_file": os.path.join(folder_dir, model_name + ".zip")}
+
+    # def control_fps(self, read_input = False):
+    #     ''' Add delay to control simulation speed '''
+
+    #     if read_input:
+    #         speed = input()
+    #         if speed == '':
+    #             self.cf_target_period = 0
+    #             print(f"Changed simulation speed to MAX")
+    #         else:
+    #             if speed == '0':
+    #                 inp = input("Paused. Set new speed or '' to use previous speed:")
+    #                 if inp != '':
+    #                     speed = inp
+
+    #             try:
+    #                 speed = int(speed)
+    #                 assert speed >= 0
+    #                 self.cf_target_period = World.STEPTIME * 100 / speed
+    #                 print(f"Changed simulation speed to {speed}%")
+    #             except:
+    #                 print("""Train_Base.py:
+    # Error: To control the simulation speed, enter a non-negative integer.
+    # To disable this control module, use test_model(..., enable_FPS_control=False) in your gyms environment.""")
+
+    #     now = time.time()
+    #     period = now - self.cf_last_time
+    #     self.cf_last_time = now
+    #     self.cf_delay += (self.cf_target_period - period)*0.9
+    #     if self.cf_delay > 0:
+    #         time.sleep(self.cf_delay)
+    #     else:
+    #         self.cf_delay = 0
+
+    def test_model(self, model: BaseAlgorithm, env, log_path: str = None, model_path: str = None, max_episodes=0,
+                   enable_FPS_control=True, verbose=1):
+        '''
+        Test model and log results
+
+        Parameters
+        ----------
+        model : BaseAlgorithm
+            Trained model
+        env : Env
+            Gym-like environment
+        log_path : str
+            Folder where statistics file is saved, default is `None` (no file is saved)
+        model_path : str
+            Folder where it reads evaluations.npz to plot it and create evaluations.csv, default is `None` (no plot, no csv)
+        max_episodes : int
+            Run tests for this number of episodes
+            Default is 0 (run until user aborts)
+        verbose : int
+            0 - no output (except if enable_FPS_control=True)
+            1 - print episode statistics
+        '''
+
+        if model_path is not None:
+            assert os.path.isdir(model_path), f"{model_path} is not a valid path"
+            self.display_evaluations(model_path)
+
+        if log_path is not None:
+            assert os.path.isdir(log_path), f"{log_path} is not a valid path"
+
+            # If file already exists, don't overwrite
+            if os.path.isfile(log_path + "/test.csv"):
+                for i in range(1000):
+                    p = f"{log_path}/test_{i:03}.csv"
+                    if not os.path.isfile(p):
+                        log_path = p
+                        break
+            else:
+                log_path += "/test.csv"
+
+            with open(log_path, 'w') as f:
+                f.write("reward,ep. length,rew. cumulative avg., ep. len. cumulative avg.\n")
+            print("Train statistics are saved to:", log_path)
+
+        if enable_FPS_control:  # control simulation speed (using non blocking user input)
+            print("\nThe simulation speed can be changed by sending a non-negative integer\n"
+                  "(e.g. '50' sets speed to 50%, '0' pauses the simulation, '' sets speed to MAX)\n")
+
+        ep_reward = 0
+        ep_length = 0
+        rewards_sum = 0
+        reward_min = math.inf
+        reward_max = -math.inf
+        ep_lengths_sum = 0
+        ep_no = 0
+
+        obs, _ = env.reset()
+        while True:
+            action, _states = model.predict(obs, deterministic=True)
+            obs, reward, terminated, truncated, info = env.step(action)
+            done = terminated or truncated
+            ep_reward += reward
+            ep_length += 1
+
+            # if enable_FPS_control:  # control simulation speed (using non blocking user input)
+            #     self.control_fps(select.select([sys.stdin], [], [], 0)[0])
+
+            if done:
+                obs, _ = env.reset()
+                rewards_sum += ep_reward
+                ep_lengths_sum += ep_length
+                reward_max = max(ep_reward, reward_max)
+                reward_min = min(ep_reward, reward_min)
+                ep_no += 1
+                avg_ep_lengths = ep_lengths_sum / ep_no
+                avg_rewards = rewards_sum / ep_no
+
+                if verbose > 0:
+                    print(
+                        f"\rEpisode: {ep_no:<3}  Ep.Length: {ep_length:<4.0f}  Reward: {ep_reward:<6.2f}                                                             \n",
+                        end=f"--AVERAGE--   Ep.Length: {avg_ep_lengths:<4.0f}  Reward: {avg_rewards:<6.2f}  (Min: {reward_min:<6.2f}  Max: {reward_max:<6.2f})",
+                        flush=True)
+
+                if log_path is not None:
+                    with open(log_path, 'a') as f:
+                        writer = csv.writer(f)
+                        writer.writerow([ep_reward, ep_length, avg_rewards, avg_ep_lengths])
+
+                if ep_no == max_episodes:
+                    return
+
+                ep_reward = 0
+                ep_length = 0
+
+    def learn_model(self, model: BaseAlgorithm, total_steps: int, path: str, eval_env=None, eval_freq=None, eval_eps=5,
+                    save_freq=None, backup_env_file=None, export_name=None):
+        '''
+        Learn Model for a specific number of time steps
+
+        Parameters
+        ----------
+        model : BaseAlgorithm
+            Model to train
+        total_steps : int
+            The total number of samples (env steps) to train on
+        path : str
+            Path where the trained model is saved
+            If the path already exists, an incrementing number suffix is added
+        eval_env : Env
+            Environment to periodically test the model
+            Default is None (no periodical evaluation)
+        eval_freq : int
+            Evaluate the agent every X steps
+            Default is None (no periodical evaluation)
+        eval_eps : int
+            Evaluate the agent for X episodes (both eval_env and eval_freq must be defined)
+            Default is 5
+        save_freq : int
+            Saves model at every X steps
+            Default is None (no periodical checkpoint)
+        backup_gym_file : str
+            Generates backup of environment file in model's folder
+            Default is None (no backup)
+        export_name : str
+            If export_name and save_freq are defined, a model is exported every X steps
+            Default is None (no export)
+
+        Returns
+        -------
+        model_path : str
+            Directory where model was actually saved (considering incremental suffix)
+
+        Notes
+        -----
+        If `eval_env` and `eval_freq` were specified:
+            - The policy will be evaluated in `eval_env` every `eval_freq` steps
+            - Evaluation results will be saved in `path` and shown at the end of training
+            - Every time the results improve, the model is saved
+        '''
+
+        start = time.time()
+        start_date = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+
+        # If path already exists, add suffix to avoid overwriting
+        if os.path.isdir(path):
+            for i in count():
+                p = path.rstrip("/") + f'_{i:03}/'
+                if not os.path.isdir(p):
+                    path = p
+                    break
+        os.makedirs(path)
+
+        # Backup environment file
+        if backup_env_file is not None:
+            backup_file = os.path.join(path, os.path.basename(backup_env_file))
+            copy(backup_env_file, backup_file)
+
+        evaluate = bool(eval_env is not None and eval_freq is not None)
+
+        # Create evaluation callback
+        eval_callback = None if not evaluate else EvalCallback(eval_env, n_eval_episodes=eval_eps, eval_freq=eval_freq,
+                                                               log_path=path,
+                                                               best_model_save_path=path, deterministic=True,
+                                                               render=False)
+
+        # Create custom callback to display evaluations
+        custom_callback = None if not evaluate else Cyclic_Callback(eval_freq,
+                                                                    lambda: self.display_evaluations(path, True))
+
+        # Create checkpoint callback
+        checkpoint_callback = None if save_freq is None else CheckpointCallback(save_freq=save_freq, save_path=path,
+                                                                                name_prefix="model", verbose=1)
+
+        # Create custom callback to export checkpoint models
+        export_callback = None if save_freq is None or export_name is None else Export_Callback(save_freq, path,
+                                                                                                export_name)
+
+        callbacks = CallbackList(
+            [c for c in [eval_callback, custom_callback, checkpoint_callback, export_callback] if c is not None])
+
+        model.learn(total_timesteps=total_steps, callback=callbacks)
+        model.save(os.path.join(path, "last_model"))
+
+        # Display evaluations if they exist
+        if evaluate:
+            self.display_evaluations(path)
+
+        # Display timestamps + Model path
+        end_date = datetime.now().strftime('%d/%m/%Y %H:%M:%S')
+        duration = timedelta(seconds=int(time.time() - start))
+        print(f"Train start:     {start_date}")
+        print(f"Train end:       {end_date}")
+        print(f"Train duration:  {duration}")
+        print(f"Model path:      {path}")
+
+        # Append timestamps to backup environment file
+        if backup_env_file is not None:
+            with open(backup_file, 'a') as f:
+                f.write(f"\n# Train start:    {start_date}\n")
+                f.write(f"# Train end:      {end_date}\n")
+                f.write(f"# Train duration: {duration}")
+
+        return path
+
+    def display_evaluations(self, path, save_csv=False):
+
+        eval_npz = os.path.join(path, "evaluations.npz")
+
+        if not os.path.isfile(eval_npz):
+            return
+
+        console_width = 80
+        console_height = 18
+        symb_x = "\u2022"
+        symb_o = "\u007c"
+        symb_xo = "\u237f"
+
+        with np.load(eval_npz) as data:
+            time_steps = data["timesteps"]
+            results_raw = np.mean(data["results"], axis=1)
+            ep_lengths_raw = np.mean(data["ep_lengths"], axis=1)
+        sample_no = len(results_raw)
+
+        xvals = np.linspace(0, sample_no - 1, 80)
+        results = np.interp(xvals, range(sample_no), results_raw)
+        ep_lengths = np.interp(xvals, range(sample_no), ep_lengths_raw)
+
+        results_limits = np.min(results), np.max(results)
+        ep_lengths_limits = np.min(ep_lengths), np.max(ep_lengths)
+
+        results_discrete = np.digitize(results, np.linspace(results_limits[0] - 1e-5, results_limits[1] + 1e-5,
+                                                            console_height + 1)) - 1
+        ep_lengths_discrete = np.digitize(ep_lengths,
+                                          np.linspace(0, ep_lengths_limits[1] + 1e-5, console_height + 1)) - 1
+
+        matrix = np.zeros((console_height, console_width, 2), int)
+        matrix[results_discrete[0]][0][0] = 1  # draw 1st column
+        matrix[ep_lengths_discrete[0]][0][1] = 1  # draw 1st column
+        rng = [[results_discrete[0], results_discrete[0]], [ep_lengths_discrete[0], ep_lengths_discrete[0]]]
+
+        # Create continuous line for both plots
+        for k in range(2):
+            for i in range(1, console_width):
+                x = [results_discrete, ep_lengths_discrete][k][i]
+                if x > rng[k][1]:
+                    rng[k] = [rng[k][1] + 1, x]
+                elif x < rng[k][0]:
+                    rng[k] = [x, rng[k][0] - 1]
+                else:
+                    rng[k] = [x, x]
+                for j in range(rng[k][0], rng[k][1] + 1):
+                    matrix[j][i][k] = 1
+
+        print(f'{"-" * console_width}')
+        for l in reversed(range(console_height)):
+            for c in range(console_width):
+                if np.all(matrix[l][c] == 0):
+                    print(end=" ")
+                elif np.all(matrix[l][c] == 1):
+                    print(end=symb_xo)
+                elif matrix[l][c][0] == 1:
+                    print(end=symb_x)
+                else:
+                    print(end=symb_o)
+            print()
+        print(f'{"-" * console_width}')
+        print(f"({symb_x})-reward          min:{results_limits[0]:11.2f}    max:{results_limits[1]:11.2f}")
+        print(
+            f"({symb_o})-ep. length      min:{ep_lengths_limits[0]:11.0f}    max:{ep_lengths_limits[1]:11.0f}    {time_steps[-1] / 1000:15.0f}k steps")
+        print(f'{"-" * console_width}')
+
+        # save CSV
+        if save_csv:
+            eval_csv = os.path.join(path, "evaluations.csv")
+            with open(eval_csv, 'a+') as f:
+                writer = csv.writer(f)
+                if sample_no == 1:
+                    writer.writerow(["time_steps", "reward ep.", "length"])
+                writer.writerow([time_steps[-1], results_raw[-1], ep_lengths_raw[-1]])
+
+    # def generate_slot_behavior(self, path, slots, auto_head:bool, XML_name):
+    #     '''
+    #     Function that generates the XML file for the optimized slot behavior, overwriting previous files
+    #     '''
+
+    #     file = os.path.join( path, XML_name )
+
+    #     # create the file structure
+    #     auto_head = '1' if auto_head else '0'
+    #     EL_behavior = ET.Element('behavior',{'description':'Add description to XML file', "auto_head":auto_head})
+
+    #     for i,s in enumerate(slots):
+    #         EL_slot = ET.SubElement(EL_behavior, 'slot', {'name':str(i), 'delta':str(s[0]/1000)})
+    #         for j in s[1]: # go through all joint indices
+    #             ET.SubElement(EL_slot, 'move', {'id':str(j), 'angle':str(s[2][j])})
+
+    #     # create XML file
+    #     xml_rough = ET.tostring( EL_behavior, 'utf-8' )
+    #     xml_pretty = minidom.parseString(xml_rough).toprettyxml(indent="  ")
+    #     with open(file, "w") as x:
+    #         x.write(xml_pretty)
+
+    #     print(file, "was created!")
+
+    # @staticmethod
+    # def linear_schedule(initial_value: float) -> Callable[[float], float]:
+    #     '''
+    #     Linear learning rate schedule
+
+    #     Parameters
+    #     ----------
+    #     initial_value : float
+    #         Initial learning rate
+
+    #     Returns
+    #     -------
+    #     schedule : Callable[[float], float]
+    #         schedule that computes current learning rate depending on remaining progress
+    #     '''
+    #     def func(progress_remaining: float) -> float:
+    #         '''
+    #         Compute learning rate according to current progress
+
+    #         Parameters
+    #         ----------
+    #         progress_remaining : float
+    #             Progress will decrease from 1 (beginning) to 0
+
+    #         Returns
+    #         -------
+    #         learning_rate : float
+    #             Learning rate according to current progress
+    #         '''
+    #         return progress_remaining * initial_value
+
+    #     return func
+
+    @staticmethod
+    def export_model(input_file, output_file, add_sufix=True):
+        '''
+        Export model weights to binary file
+
+        Parameters
+        ----------
+        input_file : str
+            Input file, compatible with algorithm
+        output_file : str
+            Output file, including directory
+        add_sufix : bool
+            If true, a suffix is appended to the file name: output_file + "_{index}.pkl"
+        '''
+
+        # If file already exists, don't overwrite
+        if add_sufix:
+            for i in count():
+                f = f"{output_file}_{i:03}.pkl"
+                if not os.path.isfile(f):
+                    output_file = f
+                    break
+
+        model = PPO.load(input_file)
+        weights = model.policy.state_dict()  # dictionary containing network layers
+
+        w = lambda name: weights[name].detach().cpu().numpy()  # extract weights from policy
+
+        var_list = []
+        for i in count(0, 2):  # add hidden layers (step=2 because that's how SB3 works)
+            if f"mlp_extractor.policy_net.{i}.bias" not in weights:
+                break
+            var_list.append(
+                [w(f"mlp_extractor.policy_net.{i}.bias"), w(f"mlp_extractor.policy_net.{i}.weight"), "tanh"])
+
+        var_list.append([w("action_net.bias"), w("action_net.weight"), "none"])  # add final layer
+
+        with open(output_file, "wb") as f:
+            pickle.dump(var_list, f, protocol=4)  # protocol 4 is backward compatible with Python 3.4
+
+    def print_list(data, numbering=True, prompt=None, divider=" | ", alignment="<", min_per_col=6):
+        '''
+        Print list - prints list, using as many columns as possible
+
+        Parameters
+        ----------
+        data : `list`
+            list of items
+        numbering : `bool`
+            assigns number to each option
+        prompt : `str`
+            the prompt string, if given, is printed after the table before reading input
+        divider : `str`
+            string that divides columns
+        alignment : `str`
+            f-string style alignment ( '<', '>', '^' )
+        min_per_col : int
+            avoid splitting columns with fewer items
+
+        Returns
+        -------
+        item : `int`, item
+            returns tuple with global index of selected item and the item object,
+            or `None` (if `numbering` or `prompt` are `None`)
+
+        '''
+
+        WIDTH = shutil.get_terminal_size()[0]
+
+        data_size = len(data)
+        items = []
+        items_len = []
+
+        # --------------------------------------------- Add numbers, margins and divider
+        for i in range(data_size):
+            number = f"{i}-" if numbering else ""
+            items.append(f"{divider}{number}{data[i]}")
+            items_len.append(len(items[-1]))
+
+        max_cols = np.clip((WIDTH + len(divider)) // min(items_len), 1, math.ceil(
+            data_size / max(min_per_col, 1)))  # width + len(divider) because it is not needed in last col
+
+        # --------------------------------------------- Check maximum number of columns, considering content width (min:1)
+        for i in range(max_cols, 0, -1):
+            cols_width = []
+            cols_items = []
+            table_width = 0
+            a, b = divmod(data_size, i)
+            for col in range(i):
+                start = a * col + min(b, col)
+                end = start + a + (1 if col < b else 0)
+                cols_items.append(items[start:end])
+                col_width = max(items_len[start:end])
+                cols_width.append(col_width)
+                table_width += col_width
+            if table_width <= WIDTH + len(divider):
+                break
+        table_width -= len(divider)
+
+        # --------------------------------------------- Print columns
+        print("=" * table_width)
+        for row in range(math.ceil(data_size / i)):
+            for col in range(i):
+                content = cols_items[col][row] if len(
+                    cols_items[col]) > row else divider  # print divider when there are no items
+                if col == 0:
+                    l = len(divider)
+                    print(end=f"{content[l:]:{alignment}{cols_width[col] - l}}")  # remove divider from 1st col
+                else:
+                    print(end=f"{content    :{alignment}{cols_width[col]}}")
+            print()
+        print("=" * table_width)
+
+        # --------------------------------------------- Prompt
+        if prompt is None:
+            return None
+
+        if numbering is None:
+            return None
+        else:
+            idx = UI.read_int(prompt, 0, data_size)
+            return idx, data[idx]
+
+
+class Cyclic_Callback(BaseCallback):
+    ''' Stable baselines custom callback '''
+
+    def __init__(self, freq, function):
+        super(Cyclic_Callback, self).__init__(1)
+        self.freq = freq
+        self.function = function
+
+    def _on_step(self) -> bool:
+        if self.n_calls % self.freq == 0:
+            self.function()
+        return True  # If the callback returns False, training is aborted early
+
+
+class Export_Callback(BaseCallback):
+    ''' Stable baselines custom callback '''
+
+    def __init__(self, freq, load_path, export_name):
+        super(Export_Callback, self).__init__(1)
+        self.freq = freq
+        self.load_path = load_path
+        self.export_name = export_name
+
+    def _on_step(self) -> bool:
+        if self.n_calls % self.freq == 0:
+            path = os.path.join(self.load_path, f"model_{self.num_timesteps}_steps.zip")
+            Train_Base.export_model(path, f"./scripts/gyms/export/{self.export_name}")
+        return True  # If the callback returns False, training is aborted early
+
+
+