Apollo3D/Gym_CPU
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

improve train speed and add speed constrain

Browse Source
This commit is contained in:
xxh
2026-03-13 08:51:49 -04:00
parent 3a42120857
commit 6ab356a947
4 changed files with 317 additions and 281 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
scripts/commons/Server.py
View File

@@ -9,7 +9,7 @@ class Server():
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 = []
@@ -18,29 +18,38 @@ class Server():
# makes it easier to kill test servers without affecting train servers
cmd = "rcssservermj"
for i in range(n_servers):
port = first_server_p + i
mport = first_monitor_p + i
server_cmd = f"{cmd} --aport {port} --mport {mport} --no-render --no-realtime"
self.rcss_processes.append(
subprocess.Popen((f"{cmd} --aport {first_server_p+i} --mport {first_monitor_p+i}").split(),
stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT, start_new_session=True)
subprocess.Popen(
server_cmd.split(),
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT,
start_new_session=True
)
)
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)
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:
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)
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]) )]
(range1[0] <= port < range1[1]) or (range2[0] <= port < range2[1]))]
if len(conflicts)>0:
if len(conflicts) > 0:
if not found:
print("\nThere are already servers running on the same port(s)!")
found = True
@@ -55,9 +64,8 @@ class Server():
for p in bad_processes:
p.kill()
return
def kill(self):
for p in self.rcss_processes:
p.kill()
print(f"Killed {self.n_servers} rcssservermj processes starting at {self.first_server_p}")
print(f"Killed {self.n_servers} rcssservermj processes starting at {self.first_server_p}")

241
scripts/commons/Train_Base.py
View File

@@ -31,31 +31,29 @@ class Train_Base():
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.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 = "./mujococodebase/scripts/gyms/logs/"
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
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]
folder_name = UI.print_list(folders, prompt="Choose folder (ctrl+c to return): ")[1]
except KeyboardInterrupt:
print()
return None # ctrl+c
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")]
@@ -64,16 +62,17 @@ class Train_Base():
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
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]
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")}
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 '''
@@ -87,7 +86,7 @@ class Train_Base():
# if speed == '0':
# inp = input("Paused. Set new speed or '' to use previous speed:")
# if inp != '':
# speed = inp
# speed = inp
# try:
# speed = int(speed)
@@ -95,7 +94,7 @@ class Train_Base():
# self.cf_target_period = World.STEPTIME * 100 / speed
# print(f"Changed simulation speed to {speed}%")
# except:
# print("""Train_Base.py:
# 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.""")
@@ -108,15 +107,15 @@ class Train_Base():
# 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):
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
Trained model
env : Env
Gym-like environment
log_path : str
@@ -147,12 +146,12 @@ class Train_Base():
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)
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")
@@ -172,8 +171,8 @@ class Train_Base():
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 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()
@@ -182,25 +181,28 @@ class Train_Base():
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
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)
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):
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
@@ -251,7 +253,7 @@ class Train_Base():
# If path already exists, add suffix to avoid overwriting
if os.path.isdir(path):
for i in count():
p = path.rstrip("/")+f'_{i:03}/'
p = path.rstrip("/") + f'_{i:03}/'
if not os.path.isdir(p):
path = p
break
@@ -265,22 +267,28 @@ class Train_Base():
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)
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))
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)
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)
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])
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") )
model.learn(total_timesteps=total_steps, callback=callbacks)
model.save(os.path.join(path, "last_model"))
# Display evaluations if they exist
if evaluate:
@@ -288,18 +296,18 @@ class Train_Base():
# Display timestamps + Model path
end_date = datetime.now().strftime('%d/%m/%Y %H:%M:%S')
duration = timedelta(seconds=int(time.time()-start))
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}")
f.write(f"# Train end: {end_date}\n")
f.write(f"# Train duration: {duration}")
return path
@@ -318,50 +326,57 @@ class Train_Base():
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)
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)
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)
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
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
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):
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]
rng[k] = [rng[k][1] + 1, x]
elif x < rng[k][0]:
rng[k] = [x, rng[k][0]-1]
rng[k] = [x, rng[k][0] - 1]
else:
rng[k] = [x,x]
for j in range(rng[k][0],rng[k][1]+1):
rng[k] = [x, x]
for j in range(rng[k][0], rng[k][1] + 1):
matrix[j][i][k] = 1
print(f'{"-"*console_width}')
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)
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'{"-" * 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}')
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:
@@ -370,8 +385,7 @@ class Train_Base():
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]])
writer.writerow([time_steps[-1], results_raw[-1], ep_lengths_raw[-1]])
# def generate_slot_behavior(self, path, slots, auto_head:bool, XML_name):
# '''
@@ -394,7 +408,7 @@ class Train_Base():
# xml_pretty = minidom.parseString(xml_rough).toprettyxml(indent=" ")
# with open(file, "w") as x:
# x.write(xml_pretty)
# print(file, "was created!")
# @staticmethod
@@ -406,7 +420,7 @@ class Train_Base():
# ----------
# initial_value : float
# Initial learning rate
# Returns
# -------
# schedule : Callable[[float], float]
@@ -420,7 +434,7 @@ class Train_Base():
# ----------
# progress_remaining : float
# Progress will decrease from 1 (beginning) to 0
# Returns
# -------
# learning_rate : float
@@ -452,28 +466,28 @@ class Train_Base():
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
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)
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(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
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`
@@ -488,7 +502,7 @@ class Train_Base():
f-string style alignment ( '<', '>', '^' )
min_per_col : int
avoid splitting columns with fewer items
Returns
-------
item : `int`, item
@@ -496,65 +510,67 @@ class Train_Base():
or `None` (if `numbering` or `prompt` are `None`)
'''
WIDTH = shutil.get_terminal_size()[0]
data_size = len(data)
data_size = len(data)
items = []
items_len = []
#--------------------------------------------- Add numbers, margins and divider
# --------------------------------------------- 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]) )
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
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):
# --------------------------------------------- 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)
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] )
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 )
cols_width.append(col_width)
table_width += col_width
if table_width <= WIDTH+len(divider):
if table_width <= WIDTH + len(divider):
break
table_width -= len(divider)
#--------------------------------------------- Print columns
print("="*table_width)
# --------------------------------------------- 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
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
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)
print(end=f"{content :{alignment}{cols_width[col]}}")
print()
print("=" * table_width)
#--------------------------------------------- Prompt
# --------------------------------------------- Prompt
if prompt is None:
return None
if numbering is None:
return None
else:
idx = UI.read_int( prompt, 0, data_size )
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
@@ -563,10 +579,12 @@ class Cyclic_Callback(BaseCallback):
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
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
@@ -577,8 +595,7 @@ class Export_Callback(BaseCallback):
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
return True # If the callback returns False, training is aborted early

271
scripts/gyms/Walk.py
View File

@@ -1,11 +1,11 @@
import os
import numpy as np
import math
from time import sleep
import time
from time import sleep
from random import random
from random import uniform
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
@@ -28,25 +28,24 @@ Learn how to run forward using step primitive
- class Train: implements algorithms to train a new model or test an existing model
'''
class WalkEnv(gym.Env):
def __init__(self, ip, server_p) -> None:
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.Player = player = Base_Agent(
team_name="Gym",
number=1,
host=ip,
port=server_p
team_name="Gym",
number=1,
host=ip,
port=server_p
)
self.robot_type = self.Player.robot
self.step_counter = 0 # to limit episode size
self.robot_type = self.Player.robot
self.step_counter = 0 # to limit episode size
self.force_play_on = True
self.target_position = np.array([0.0, 0.0]) # target position in the x-y plane
self.initial_position = np.array([0.0, 0.0]) # initial position in the x-y plane
self.target_direction = 0.0 # target direction in the x-y plane (relative to the robot's orientation)
self.target_position = np.array([0.0, 0.0]) # target position in the x-y plane
self.initial_position = np.array([0.0, 0.0]) # initial position in the x-y plane
self.target_direction = 0.0 # target direction in the x-y plane (relative to the robot's orientation)
self.isfallen = False
self.waypoint_index = 0
self.route_completed = False
@@ -55,16 +54,26 @@ class WalkEnv(gym.Env):
self.auto_calibrate_train_sim_flip = True
self.nominal_calibrated_once = False
self.flip_calibrated_once = False
self._target_hz = 0.0
self._target_dt = 0.0
self._last_sync_time = None
target_hz_env = 24
if target_hz_env:
try:
self._target_hz = float(target_hz_env)
except ValueError:
self._target_hz = 0.0
if self._target_hz > 0.0:
self._target_dt = 1.0 / self._target_hz
# State space
# 原始观测大小: 78
obs_size = 78
self.obs = np.zeros(obs_size, np.float32)
self.observation_space = spaces.Box(
low=-10.0,
high=10.0,
shape=(obs_size,),
low=-10.0,
high=10.0,
shape=(obs_size,),
dtype=np.float32
)
@@ -73,11 +82,10 @@ class WalkEnv(gym.Env):
self.action_space = spaces.Box(
low=-1.0,
high=1.0,
shape=(action_dim,),
shape=(action_dim,),
dtype=np.float32
)
# 中立姿态
self.joint_nominal_position = np.array(
[
@@ -110,22 +118,22 @@ class WalkEnv(gym.Env):
self.train_sim_flip = np.array(
[
1.0, # 0: Head_yaw (he1)
1.0, # 0: Head_yaw (he1)
-1.0, # 1: Head_pitch (he2)
1.0, # 2: Left_Shoulder_Pitch (lae1)
1.0, # 2: Left_Shoulder_Pitch (lae1)
-1.0, # 3: Left_Shoulder_Roll (lae2)
1.0, # 4: Left_Elbow_Pitch (lae3)
1.0, # 5: Left_Elbow_Yaw (lae4)
1.0, # 4: Left_Elbow_Pitch (lae3)
1.0, # 5: Left_Elbow_Yaw (lae4)
-1.0, # 6: Right_Shoulder_Pitch (rae1)
1.0, # 7: Right_Shoulder_Roll (rae2)
1.0, # 8: Right_Elbow_Pitch (rae3)
1.0, # 9: Right_Elbow_Yaw (rae4)
1.0, # 10: Waist (te1)
1.0, # 11: Left_Hip_Pitch (lle1)
1.0, # 7: Right_Shoulder_Roll (rae2)
1.0, # 8: Right_Elbow_Pitch (rae3)
1.0, # 9: Right_Elbow_Yaw (rae4)
1.0, # 10: Waist (te1)
1.0, # 11: Left_Hip_Pitch (lle1)
-1.0, # 12: Left_Hip_Roll (lle2)
-1.0, # 13: Left_Hip_Yaw (lle3)
1.0, # 14: Left_Knee_Pitch (lle4)
1.0, # 15: Left_Ankle_Pitch (lle5)
1.0, # 14: Left_Knee_Pitch (lle4)
1.0, # 15: Left_Ankle_Pitch (lle5)
-1.0, # 16: Left_Ankle_Roll (lle6)
-1.0, # 17: Right_Hip_Pitch (rle1)
-1.0, # 18: Right_Hip_Roll (rle2)
@@ -141,7 +149,7 @@ class WalkEnv(gym.Env):
self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
self.previous_pos = np.array([0.0, 0.0]) # Track previous position
self.Player.server.connect()
sleep(2.0) # Longer wait for connection to establish completely
# sleep(2.0) # Longer wait for connection to establish completely
self.Player.server.send_immediate(
f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
)
@@ -160,7 +168,7 @@ class WalkEnv(gym.Env):
changed = []
for idx, (reference_value, observed_value) in enumerate(
zip(self.reference_joint_nominal_position, neutral_joint_positions)
zip(self.reference_joint_nominal_position, neutral_joint_positions)
):
if idx >= 10:
continue
@@ -187,38 +195,37 @@ class WalkEnv(gym.Env):
height = float(self.Player.world.global_position[2])
reliable = (
leg_norm > 0.8
and leg_max > 0.35
and 0.12 < height < 0.8
leg_norm > 0.8
and leg_max > 0.35
and 0.12 < height < 0.8
)
return reliable, leg_norm, leg_max, height
def observe(self, init=False):
"""获取当前观测值"""
robot = self.Player.robot
world = self.Player.world
# Safety check: ensure data is available
# 计算目标速度
raw_target = self.target_position - world.global_position[:2]
velocity = MathOps.rotate_2d_vec(
raw_target,
-robot.global_orientation_euler[2],
raw_target,
-robot.global_orientation_euler[2],
is_rad=False
)
# 计算相对方向
rel_orientation = MathOps.vector_angle(velocity) * 0.3
rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
velocity = np.concatenate([velocity, np.array([rel_orientation])])
velocity[0] = np.clip(velocity[0], -0.5, 0.5)
velocity[1] = np.clip(velocity[1], -0.25, 0.25)
# 关节状态
radian_joint_positions = np.deg2rad(
[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
@@ -226,21 +233,20 @@ class WalkEnv(gym.Env):
radian_joint_speeds = np.deg2rad(
[robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
)
qpos_qvel_previous_action = np.concatenate([
(radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
radian_joint_speeds / 110.0 * self.train_sim_flip,
self.previous_action / 10.0,
])
# 角速度
ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
# 投影的重力方向
orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
# 组合观测
observation = np.concatenate([
qpos_qvel_previous_action,
@@ -249,8 +255,6 @@ class WalkEnv(gym.Env):
projected_gravity,
])
observation = np.clip(observation, -10.0, 10.0)
return observation.astype(np.float32)
@@ -260,6 +264,17 @@ class WalkEnv(gym.Env):
self.Player.world.update()
self.Player.robot.commit_motor_targets_pd()
self.Player.server.send()
if self._target_dt > 0.0:
now = time.time()
if self._last_sync_time is None:
self._last_sync_time = now
return
elapsed = now - self._last_sync_time
remaining = self._target_dt - elapsed
if remaining > 0.0:
time.sleep(remaining)
now = time.time()
self._last_sync_time = now
def debug_joint_status(self):
robot = self.Player.robot
@@ -295,12 +310,11 @@ class WalkEnv(gym.Env):
if seed is not None:
np.random.seed(seed)
length1 = np.random.uniform(10, 20) # randomize target distance
length2 = np.random.uniform(10, 20) # randomize target distance
length3 = np.random.uniform(10, 20) # randomize target distance
angle2 = np.random.uniform(-30, 30) # randomize initial orientation
angle3 = np.random.uniform(-30, 30) # randomize target direction
length1 = np.random.uniform(10, 20) # randomize target distance
length2 = np.random.uniform(10, 20) # randomize target distance
length3 = np.random.uniform(10, 20) # randomize target distance
angle2 = np.random.uniform(-30, 30) # randomize initial orientation
angle3 = np.random.uniform(-30, 30) # randomize target direction
self.step_counter = 0
self.waypoint_index = 0
@@ -322,12 +336,12 @@ class WalkEnv(gym.Env):
# 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
finished_count = 0
for _ in range(20):
for _ in range(10):
finished = self.Player.skills_manager.execute("Neutral")
self.sync()
if finished:
finished_count += 1
if finished_count >= 2: # 假设需要连续2次完成才算成功
if finished_count >= 3: # 假设需要连续3次完成才算成功
break
# neutral_joint_positions = np.deg2rad(
@@ -356,17 +370,15 @@ class WalkEnv(gym.Env):
# reset_action_noise = np.random.uniform(-0.015, 0.015, size=(len(self.Player.robot.ROBOT_MOTORS),))
# self.target_joint_positions = (self.joint_nominal_position + reset_action_noise) * self.train_sim_flip
# for idx, target in enumerate(self.target_joint_positions):
# r.set_motor_target_position(
# r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
# )
# memory variables
self.initial_position = np.array(self.Player.world.global_position[:2])
self.previous_pos = self.initial_position.copy() # Critical: set to actual position
self.act = np.zeros(self.no_of_actions ,np.float32)
self.act = np.zeros(self.no_of_actions, np.float32)
point1 = self.initial_position + np.array([length1, 0])
point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
@@ -423,57 +435,51 @@ class WalkEnv(gym.Env):
tilt_penalty = -0.2 * np.linalg.norm(self.Player.robot.gyroscope[:2]) / 100.0
return (
progress_reward
+ speed_reward
+ direction_reward
+ alive_bonus
+ height_reward
+ motionless_penalty
+ waypoint_bonus
+ action_penalty
+ tilt_penalty
progress_reward
+ speed_reward
+ direction_reward
+ alive_bonus
+ height_reward
+ motionless_penalty
+ waypoint_bonus
+ action_penalty
+ tilt_penalty
)
def step(self, action):
r = self.Player.robot
r = self.Player.robot
self.previous_action = action
self.target_joint_positions = (
self.joint_nominal_position
+ self.scaling_factor * action
self.joint_nominal_position
+ self.scaling_factor * action
)
self.target_joint_positions *= self.train_sim_flip
for idx, target in enumerate(self.target_joint_positions):
r.set_motor_target_position(
r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
)
self.sync() # run simulation step
self.sync() # run simulation step
self.step_counter += 1
# if self.step_counter % self.debug_every_n_steps == 0:
# self.debug_joint_status()
current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
# Compute reward based on movement from previous step
reward = self.compute_reward(self.previous_pos, current_pos, action)
# Update previous position
self.previous_pos = current_pos.copy()
# Fall detection and penalty
is_fallen = self.Player.world.global_position[2] < 0.3
# terminal state: the robot is falling or timeout
terminated = is_fallen or self.step_counter > 800 or self.route_completed
truncated = False
@@ -481,64 +487,65 @@ class WalkEnv(gym.Env):
return self.observe(), reward, terminated, truncated, {}
class Train(Train_Base):
def __init__(self, script) -> None:
super().__init__(script)
def train(self, args):
#--------------------------------------- Learning parameters
# --------------------------------------- Learning parameters
n_envs = 8 # Reduced from 8 to decrease CPU/network pressure during init
if n_envs < 1:
raise ValueError("GYM_CPU_N_ENVS must be >= 1")
n_steps_per_env = 512 # RolloutBuffer is of size (n_steps_per_env * n_envs)
minibatch_size = 64 # should be a factor of (n_steps_per_env * n_envs)
minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs)
total_steps = 30000000
learning_rate = 3e-4
folder_name = f'Walk_R{self.robot_type}'
model_path = f'./scripts/gyms/logs/{folder_name}/'
model_path = f'./scripts/gyms/logs/{folder_name}/'
print(f"Model path: {model_path}")
print(f"Using {n_envs} parallel environments")
#--------------------------------------- Run algorithm
# --------------------------------------- Run algorithm
def init_env(i_env):
def thunk():
return WalkEnv( self.ip , self.server_p + i_env)
return WalkEnv(self.ip, self.server_p + i_env)
return thunk
servers = Train_Server( self.server_p, self.monitor_p_1000, n_envs+1 ) #include 1 extra server for testing
server_log_dir = os.path.join(model_path, "server_logs")
os.makedirs(server_log_dir, exist_ok=True)
servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1) # include 1 extra server for testing
# Wait for servers to start
print(f"Starting {n_envs+1} rcssservermj servers...")
print(f"Starting {n_envs + 1} rcssservermj servers...")
print("Servers started, creating environments...")
env = SubprocVecEnv( [init_env(i) for i in range(n_envs)] )
eval_env = SubprocVecEnv( [init_env(n_envs)] )
env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
eval_env = SubprocVecEnv([init_env(n_envs)])
try:
# Custom policy network architecture
policy_kwargs = dict(
net_arch=dict(
pi=[256, 256, 128], # Policy network: 3 layers
vf=[256, 256, 128] # Value network: 3 layers
vf=[256, 256, 128] # Value network: 3 layers
),
activation_fn=__import__('torch.nn', fromlist=['ReLU']).ReLU,
)
if "model_file" in args: # retrain
model = PPO.load( args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
else: # train new model
model = PPO(
"MlpPolicy",
env=env,
verbose=1,
n_steps=n_steps_per_env,
batch_size=minibatch_size,
learning_rate=learning_rate,
if "model_file" in args: # retrain
model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
batch_size=minibatch_size, learning_rate=learning_rate)
else: # train new model
model = PPO(
"MlpPolicy",
env=env,
verbose=1,
n_steps=n_steps_per_env,
batch_size=minibatch_size,
learning_rate=learning_rate,
device="cpu",
policy_kwargs=policy_kwargs,
ent_coef=0.01, # Entropy coefficient for exploration
@@ -547,28 +554,32 @@ class Train(Train_Base):
gamma=0.99 # Discount factor
)
model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*20, save_freq=n_steps_per_env*20, backup_env_file=__file__ )
model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
backup_env_file=__file__)
except KeyboardInterrupt:
sleep(1) # wait for child processes
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
servers.kill()
return
env.close()
eval_env.close()
servers.kill()
def test(self, args):
# Uses different server and monitor ports
server = Train_Server( self.server_p-1, self.monitor_p, 1 )
env = WalkEnv( self.ip, self.server_p-1 )
model = PPO.load( args["model_file"], env=env )
server_log_dir = os.path.join(args["folder_dir"], "server_logs")
os.makedirs(server_log_dir, exist_ok=True)
server = Train_Server(self.server_p - 1, self.monitor_p, 1, log_dir=server_log_dir)
env = WalkEnv(self.ip, self.server_p - 1)
model = PPO.load(args["model_file"], env=env)
try:
self.export_model( args["model_file"], args["model_file"]+".pkl", False ) # Export to pkl to create custom behavior
self.test_model( model, env, log_path=args["folder_dir"], model_path=args["folder_dir"] )
self.export_model(args["model_file"], args["model_file"] + ".pkl",
False) # Export to pkl to create custom behavior
self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
except KeyboardInterrupt:
print()
@@ -578,18 +589,18 @@ class Train(Train_Base):
if __name__ == "__main__":
from types import SimpleNamespace
# 创建默认参数
script_args = SimpleNamespace(
args=SimpleNamespace(
i='127.0.0.1', # Server IP
p=3100, # Server port
m=3200, # Monitor port
r=0, # Robot type
t='Gym', # Team name
u=1 # Uniform number
p=3100, # Server port
m=3200, # Monitor port
r=0, # Robot type
t='Gym', # Team name
u=1 # Uniform number
)
)
trainer = Train(script_args)
trainer.train({})
trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_000/model_245760_steps.zip"})