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import copy
import json
import os
import shutil
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, CheckpointCallback
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 = "CustomMyoBaodingBallsP2"
# saving criteria
saving_criteria = "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 and best model (if not first task)
# load_folder = "trained_models/baoding_phase2/00-45-16_final_nisheet_score-36"
# PATH_TO_NORMALIZED_ENV = load_folder + "/training_env.pkl"
# PATH_TO_PRETRAINED_NET = load_folder + "/best_model.zip"
# Path to normalized Vectorized environment (if not first task)
PATH_TO_NORMALIZED_ENV = "output/training/2022-11-02/10-47-05_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5/final_env.pkl" # "trained_models/normalized_env_original"
# Path to pretrained network (if not first task)
PATH_TO_PRETRAINED_NET = "output/training/2022-11-02/10-47-05_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5/final_model.pkl" # "trained_models/best_model.zip"
# Tensorboard log (will save best model during evaluation)
now = (
datetime.now().strftime("%Y-%m-%d/%H-%M-%S")
+ "_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5_resume"
)
TENSORBOARD_LOG = os.path.join("output", "training", now)
# Reward structure and task parameters:
config = {
"weighted_reward_keys": {
"pos_dist_1": 5,
"pos_dist_2": 5,
"act_reg": 0,
"alive": 1,
"solved": 5,
"done": 0,
"sparse": 0,
},
# custom params for curriculum learning
"enable_rsi": False,
"rsi_probability": 0,
"balls_overlap": False,
"overlap_probability": 0,
"noise_fingers": 0,
"limit_init_angle": np.pi,
# "beta_init_angle": [1.5,1.5], # caution: doesn't work if limit_init_angle = False
"goal_time_period": [4, 6], # phase 2: (4, 6)
"goal_xrange": (0.020, 0.030), # phase 2: (0.020, 0.030)
"goal_yrange": (0.022, 0.032), # phase 2: (0.022, 0.032)
# Randomization in physical properties of the baoding balls
"obj_size_range": (
0.018,
0.024,
), # (0.018, 0.024 # Object size range. Nominal 0.022
# "beta_ball_size": [1.5,1.5],
"obj_mass_range": (
0.030,
0.300,
), # (0.030, 0.300) # Object weight range. Nominal 43 gms
# "beta_ball_mass": [1.5,1.5],
"obj_friction_change": (0.2, 0.001, 0.00002), # (0.2, 0.001, 0.00002)
# Task
"task_choice": "random",
}
# Function that creates and monitors vectorized environments:
def make_parallel_envs(
env_name, env_config, num_env, start_index=0
): # pylint: disable=redefined-outer-name
def make_env(_):
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: # pylint: disable=unspecified-encoding
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.update(
{
"weighted_reward_keys": {
"pos_dist_1": 0,
"pos_dist_2": 0,
"act_reg": 0,
"solved": 5,
"alive": 0,
"done": 0,
"sparse": 0,
},
# score on the final noise distribution
"noise_fingers": 0,
"limit_init_angle": False,
"beta_init_angle": False,
"beta_ball_size": False,
"beta_ball_mass": False,
}
)
config_effort.update(
{
"weighted_reward_keys": {
"pos_dist_1": 0,
"pos_dist_2": 0,
"act_reg": 1,
"solved": 0,
"alive": 0,
"done": 0,
"sparse": 0,
},
# effort on the final noise distribution
"noise_fingers": 0,
"limit_init_angle": False,
"beta_init_angle": False,
"beta_ball_size": False,
"beta_ball_mass": False,
}
)
env_score = EnvironmentFactory.create(env_name, **config_score)
env_effort = EnvironmentFactory.create(env_name, **config_effort)
score_callback = EvaluateLSTM(
eval_freq=50000, eval_env=env_score, name="eval/score", num_episodes=20
)
effort_callback = EvaluateLSTM(
eval_freq=5000, eval_env=env_effort, name="eval/effort", num_episodes=1
)
# Evaluation Callback
# Create vectorized environments:
if saving_criteria == "score":
eval_envs = make_parallel_envs(env_name, config_score, num_env=1)
elif saving_criteria == "dense_rewards":
eval_envs = make_parallel_envs(env_name, config, num_env=1)
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=2_500,
deterministic=True,
render=False,
n_eval_episodes=20,
)
checkpoint_callback = CheckpointCallback(
save_freq=10000, save_path=TENSORBOARD_LOG, save_vecnormalize=True
)
# 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:
custom_objects = {
"lr_schedule": lambda _: 5e-05,
"learning_rate": lambda _: 5e-05,
"clip_range": 0.3,
"n_steps": 4096,
"batch_size": 4096,
"ent_coef": 0.00025,
"vf_coef": 1,
}
model = RecurrentPPO.load(
PATH_TO_PRETRAINED_NET,
env=envs,
tensorboard_log=TENSORBOARD_LOG,
device="cuda",
custom_objects=custom_objects,
)
# Train and save model
model.learn(
total_timesteps=50_000_000,
# callback=[eval_callback, score_callback, effort_callback, checkpoint_callback],
callback=[score_callback, checkpoint_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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