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import json
import os
import shutil
from calendar import c
from datetime import datetime
import numpy as np
import torch.nn as nn
from sb3_contrib import RecurrentPPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import VecNormalize
from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
from src.envs.environment_factory import EnvironmentFactory
from src.metrics.custom_callbacks import EvaluateLSTM
from src.metrics.sb_callbacks import EnvDumpCallback
env_name = "CustomMyoBaodingBallsP1"
# saving criteria
saving_criteria = "dense_rewards" # dense_rewards, score
# whether this is the first task of the curriculum (True) or it is loading a previous task (False)
FIRST_TASK = False
# Path to normalized Vectorized environment (if not first task)
PATH_TO_NORMALIZED_ENV = "trained_models/random/08-28-26_random_6_6/training_env.pkl"
# Path to pretrained network (if not first task)
PATH_TO_PRETRAINED_NET = "trained_models/random/08-28-26_random_6_6/best_model.zip"
# Tensorboard log (will save best model during evaluation)
now = datetime.now().strftime("%Y-%m-%d/%H-%M-%S") + "_random_5_5"
TENSORBOARD_LOG = os.path.join("output", "training", now)
# Reward structure and task parameters:
config = {
"weighted_reward_keys": {
"pos_dist_1": 1,
"pos_dist_2": 1,
"act_reg": 0,
"alive": 1,
"solved": 5,
"done": 0,
"sparse": 0,
},
"task": "random",
"enable_rsi": False,
"rsi_probability": 0,
"noise_palm": 0,
"noise_fingers": 0,
"noise_balls": 0,
"goal_time_period": [5, 5], # phase 2: (4, 6)
"goal_xrange": (0.025, 0.025), # phase 2: (0.020, 0.030)
"goal_yrange": (0.028, 0.028), # phase 2: (0.022, 0.032)
"drop_th": 1.3,
}
# Function that creates and monitors vectorized environments:
def make_parallel_envs(env_name, env_config, num_env, start_index=0):
def make_env(rank):
def _thunk():
env = EnvironmentFactory.register(env_name, **env_config)
env = Monitor(env, TENSORBOARD_LOG)
return env
return _thunk
return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
if __name__ == "__main__":
os.makedirs(TENSORBOARD_LOG, exist_ok=True)
with open(os.path.join(TENSORBOARD_LOG, "config.json"), "w") as file:
json.dump(config, file)
shutil.copy(os.path.abspath(__file__), TENSORBOARD_LOG)
# Create vectorized environments:
envs = make_parallel_envs(env_name, config, num_env=16)
# Normalize environment:
if FIRST_TASK:
envs = VecNormalize(envs)
else:
envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, envs)
# Callbacks for score and for effort
config_score, config_effort = copy.deepcopy(config), copy.deepcopy(config)
config_score['weighted_reward_keys'].update({
'pos_dist_1': 0,
'pos_dist_2': 0,
'act_reg': 0,
'solved': 5,
'alive':0,
'done': 0,
'sparse': 0})
config_effort['weighted_reward_keys'].update({
'pos_dist_1': 0,
'pos_dist_2': 0,
'act_reg': 1,
'solved': 0,
'alive':0,
'done': 0,
'sparse': 0})
env_score = EnvironmentFactory.register(env_name, **config_score)
env_effort = EnvironmentFactory.register(env_name, **config_effort)
score_callback = EvaluateLSTM(eval_freq = 5000, eval_env = env_score, name = 'eval/score', num_episodes=10)
effort_callback = EvaluateLSTM(eval_freq = 5000, eval_env = env_effort, name = 'eval/effort', num_episodes=10)
# Evaluation Callback
# Create vectorized environments:
if saving_criteria=="score":
eval_envs = make_parallel_envs(env_name, config_score, num_env=16)
elif saving_criteria=="dense_rewards":
eval_envs = make_parallel_envs(env_name, config, num_env=16)
else:
raise ValueError('Unrecognized saving criteria')
if FIRST_TASK:
eval_envs = VecNormalize(eval_envs)
else:
eval_envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, eval_envs)
env_dump_callback = EnvDumpCallback(TENSORBOARD_LOG, verbose=0)
eval_callback = EvalCallback(
eval_envs,
callback_on_new_best=env_dump_callback,
best_model_save_path=TENSORBOARD_LOG,
log_path=TENSORBOARD_LOG,
eval_freq=2500,
deterministic=True,
render=False,
n_eval_episodes=20,
)
# Create model (hyperparameters from RL Zoo HalfCheetak)
if FIRST_TASK:
model = RecurrentPPO(
"MlpLstmPolicy",
envs,
verbose=2,
tensorboard_log=TENSORBOARD_LOG,
batch_size=32,
n_steps=512,
gamma=0.99,
gae_lambda=0.9,
n_epochs=10,
ent_coef= 3e-6,
learning_rate=2e-5,
clip_range=0.25,
use_sde=True,
max_grad_norm=0.8,
vf_coef=0.5,
policy_kwargs=dict(
log_std_init=-2,
ortho_init=False,
activation_fn=nn.ReLU,
net_arch=[dict(pi=[], vf=[])],
enable_critic_lstm=True,
lstm_hidden_size=128,
),
)
else:
model = RecurrentPPO.load(
PATH_TO_PRETRAINED_NET, env=envs, tensorboard_log=TENSORBOARD_LOG, device='cuda'
)
# Train and save model
model.learn(
total_timesteps=10_000_000, callback=[eval_callback,score_callback,effort_callback], reset_num_timesteps=True
)
model.save(os.path.join(TENSORBOARD_LOG, "final_model.pkl"))
envs.save(os.path.join(TENSORBOARD_LOG, "final_env.pkl"))
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