from gym.spaces import Box, Discrete
from models.cc_concatenate import CCPPOPolicy, CCPPOTrainer
from ray.rllib.agents.ppo.ppo import PPOTrainer
import numpy as np
import time
import yaml
import tensorflow as tf
from utils.loader import load_envs, load_models
from ray.rllib.agents.registry import get_agent_class
#from ray.rllib.agents.marwil.marwil import MARWILTrainer
from imitation_agent.imitation_trainer import ImitationAgent
tf.compat.v1.enable_eager_execution()
#tf.compat.v1.enable_v2_behavior()
#tf.compat.v1.disable_eager_execution()
load_envs("envs/flatland")
load_models()
ray.init()
with open("custom_imitation_learning_rllib_tree_obs/ppo_imitation_tree_obs.yaml") as f:
config = yaml.safe_load(f)
obs_config = config["flatland-sparse-small-tree-fc-apex-il-trainer"]["config"]["env_config"]["observation_config"]
agent_config = config["flatland-sparse-small-tree-fc-apex-il-trainer"]["config"]["model"]
#####################################################################
# Instantiate a Remote Client
#####################################################################
remote_client = FlatlandRemoteClient()
#####################################################################
# Instantiate your custom Observation Builder
#
# You can build your own Observation Builder by following
##################################### Setup #####################################################
#######################################################################################################
EXAMPLE_USAGE = """
Training example:
python ./train.py --run DQN --env CartPole-v0
Training with Config:
python ./train.py -f experiments/simple-corridor-0.yaml
Note that -f overrides all other trial-specific command-line options.
"""
# Register all necessary assets in tune registries
load_envs(os.getcwd()) # Load envs
load_models(os.getcwd()) # Load models
# Load custom algorithms
from algorithms import CUSTOM_ALGORITHMS
load_algorithms(CUSTOM_ALGORITHMS)
print(ray.rllib.contrib.registry.CONTRIBUTED_ALGORITHMS)
#######################################################################################################
##################################### Helper Funcs #################################################
#######################################################################################################
def create_parser(parser_creator=None):
parser = make_parser(parser_creator=parser_creator,
formatter_class=argparse.RawDescriptionHelpFormatter,
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from utils.loader import load_envs, load_models, load_algorithms, load_preprocessors
from ray_experiment_builder import RayExperimentBuilder
# Try to import both backends for flag checking/warnings.
tf = try_import_tf()
torch, _ = try_import_torch()
from custom.models.my_vision_network import MyVisionNetwork
"""
Note : This script has been adapted from :
https://github.com/ray-project/ray/blob/master/rllib/train.py and https://github.com/AIcrowd/neurips2020-procgen-starter-kit/blob/master/train.py
"""
load_envs(os.path.join("custom", "envs"))
load_models((os.path.join("custom", "models"))) # Load models
# Load custom algorithms
from custom.algorithms import CUSTOM_ALGORITHMS
load_algorithms(CUSTOM_ALGORITHMS)
# Load custom preprocessors
from custom.preprocessors import CUSTOM_PREPROCESSORS
load_preprocessors(CUSTOM_PREPROCESSORS)
print(ray.rllib.contrib.registry.CONTRIBUTED_ALGORITHMS)
def run():
ModelCatalog.register_custom_model("my_vision_network", MyVisionNetwork)
config = {
"model": {
