test_digging_dict_mask_server.py

import os
from random import seed
import numpy as np
from gym.envs.registration import make
from stable_baselines3 import SAC, PPO, A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.policies import ActorCriticPolicy
from stable_baselines3.sac.policies import MlpPolicy
import gym
from stable_baselines3.common.vec_env import (
    SubprocVecEnv,
    VecTransposeImage,
    DummyVecEnv,
)
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3 import TD3
from stable_baselines3.common import results_plotter
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import (
    SubprocVecEnv,
    DummyVecEnv,
    VecTransposeImage,
)
from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results
from stable_baselines3.common.callbacks import (
    CallbackList,
    CheckpointCallback,
    EvalCallback,
)
from stable_baselines3.common.env_checker import check_env
from custom_callbacks import VideoRecorderCallback, TensorboardCallback
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
import tensorflow as tf
from helpers import make_boxworld, make_env, parallel_boxworlds, parallel_worlds
from net import RelationalNet
import torch as th
import torch.nn as nn
import matplotlib.pyplot as plt
from extractors import (
    RelationalNet,
    DeeperExtractor,
    SimpleExtractor,
    SimpleExtractorDict,
    CustomCombinedExtractor,
    CustomDeeperCombinedExtractor
)
import heightgrid  # register the environment
from masked_policy import MaskedActorCriticPolicy

# checkpoint (model and replay buffer): check
# logging (personalized): -
# on custom environment


if __name__ == "__main__":
    log_dir = "cluster/home/lterenzi/git/Relational-Deep-RL/logs/heightgrid/ppo/digging_16x16/dict_mask"
    os.makedirs(log_dir, exist_ok=True)
    size = 16
    num_digging_pts = 8

    env_id = "HeightGrid-RandomTargetHeight-v0"
    env = parallel_worlds(
        env_id,
        log_dir=log_dir,
        flat_obs=False,
        num_envs=24,
        size=size,
        step_cost=-0.001,
        mask=True,
        num_digging_pts=num_digging_pts,
        max_steps=1024,
    )

    eval_env = make_env(
        env_id,
        log_dir=log_dir,
        seed=24,
        flat_obs=False,
        size=size,
        step_cost=-0.001,
        mask=True,
        num_digging_pts=num_digging_pts,
        max_steps=2*1024,

    )()
    # figure, ax = eval_env.render()
    # plt.plot(figure)
    print("eval obs ", eval_env.observation_space)
    policy_kwargs = dict(
        features_extractor_class=CustomCombinedExtractor,
        net_arch=[256, 256, 256, dict(pi=[256], vf=[256])],

    )

    # with steps 2058 * num_envs
    name_prefix = "mask_pts" + str(num_digging_pts) 
    checkpoint_callback = CheckpointCallback(
        save_freq=200000, save_path=log_dir, name_prefix=name_prefix
    )

    # Separate evaluation env

    # eval_env.render('human')
    # check_env(eval_env)
    # print("Created eval env")
    eval_callback = EvalCallback(
        eval_env,
        best_model_save_path=log_dir + "/best_model",
        log_path=log_dir + "/results",
        eval_freq=20000,
    )

    # not saving
    video_recorder = VideoRecorderCallback(eval_env, render_freq=20000)
    callbacks = CallbackList([eval_callback, checkpoint_callback])
    continue_training = 0

    if continue_training:
        model_log_dir = "logs/heightgrid/ppo/digging_8x8/dict_mask/ppo_dig_mask_12800000_steps"
        env.reset()
        model = PPO.load(model_log_dir, env=env)

        model.learn(
            20e7,
            callback=callbacks,
            tb_log_name="ppo_dig",
            reset_num_timesteps=False,
        )

    else:

        model = PPO(
            MaskedActorCriticPolicy,
            env,
            gamma=0.997,
            batch_size=2*2048,
            n_steps=2*1024,
            n_epochs=4,
            ent_coef=0.001,
            learning_rate=0.0003,
            policy_kwargs=policy_kwargs,
            verbose=1,
            create_eval_env=True,
            tensorboard_log=log_dir,
        )
        # model.load('logs/heightgrid/ppo/digging_8x8/ppo_goal_target_9600000_steps', env=env)
        model.learn(
            20e7,
            callback=callbacks,
            tb_log_name="ppo_dig",
            reset_num_timesteps=True,
        )

    # save the model
    model.save(log_dir + "/ppo_dig_model")
    # now save the replay buffer too
    # model.save_replay_buffer("sac_replay_buffer")

    # # load the model
    # loaded_model = PPO.load("ppo_boxworld", env=env)
    # # load it into the loaded_model
    # # loaded_model.load_replay_buffer("sac_replay_buffer")

    # loaded_model.learn(
    #     total_timesteps=7000, tb_log_name="second", reset_num_timesteps=False
    # )

    # # Retrieve the environment
    # env = model.get_env()

    # # Evaluate the policy
    # mean_reward, std_reward = evaluate_policy(
    #     model.policy, eval_env, n_eval_episodes=10, deterministic=True
    # )
    # print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

    # obs = eval_env.reset()
    # for i in range(1000):
    #     action, _states = model.predict(obs, deterministic=True)
    #     obs, rewards, dones, info = eval_env.step(action)
    #     env.render("human")