#!/usr/bin/env python3
# Copyright (c) 2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of L2RPN Baselines, L2RPN Baselines a repository to host baselines for l2rpn competitions.
import os
from grid2op.Runner import Runner
from l2rpn_baselines.Template.template import Template
from l2rpn_baselines.utils.save_log_gif import save_log_gif
[docs]def evaluate(env,
load_path=".",
logs_path=None,
nb_episode=1,
nb_process=1,
max_steps=-1,
verbose=False,
save_gif=False,
**kwargs):
"""
In order to submit a valid basline, it is mandatory to provide a "evaluate" function with the same signature as this one.
Parameters
----------
env: :class:`grid2op.Environment.Environment`
The environment on which the baseline will be evaluated.
load_path: ``str``
The path where the model is stored. This is used by the agent when calling "agent.load)
logs_path: ``str``
The path where the agents results will be stored.
nb_episode: ``int``
Number of episodes to run for the assessment of the performance.
By default it's 1.
nb_process: ``int``
Number of process to be used for the assessment of the performance.
Should be an integer greater than 1. By defaults it's 1.
max_steps: ``int``
Maximum number of timestep each episode can last. It should be a positive integer or -1.
-1 means that the entire episode is run (until the chronics is out of data or until a game over).
By default it's -1.
verbose: ``bool``
verbosity of the output
save_gif: ``bool``
Whether or not to save a gif into each episode folder corresponding to the representation of the said episode.
kwargs:
Other key words arguments that you are free to use for either building the agent save it etc.
Returns
-------
``None``
"""
runner_params = env.get_params_for_runner()
runner_params["verbose"] = verbose
# Create the agent (this piece of code can change)
agent = Template(env.action_space, env.observation_space, "Template")
# Load weights from file (for example)
agent.load(load_path)
# Build runner
runner = Runner(**runner_params,
agentClass=None,
agentInstance=agent)
# you can do stuff with your model here
# start the runner
res = runner.run(path_save=logs_path,
nb_episode=nb_episode,
nb_process=nb_process,
max_iter=max_steps,
pbar=False)
# Print summary
print("Evaluation summary:")
for _, chron_name, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics located at {}\n".format(chron_name)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
if save_gif:
save_log_gif(logs_path, res)
if __name__ == "__main__":
"""
This is a possible implementation of the eval script.
"""
import grid2op
from l2rpn_baselines.utils import cli_eval
args_cli = cli_eval().parse_args()
env = grid2op.make()
evaluate(env,
load_path=args_cli.load_path,
logs_path=args_cli.logs_path,
nb_episode=args_cli.nb_episode,
nb_process=args_cli.nb_process,
max_steps=args_cli.max_steps,
verbose=args_cli.verbose,
save_gif=args_cli.save_gif)