runner.py

"""
MIT License from https://github.com/marmotlab/CAtNIPP/

Copyright (c) 2022 MARMot Lab @ NUS-ME

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import torch
import numpy as np
import ray
import os
from attention_net import AttentionNet
from worker import Worker
from parameters import *


class Runner(object):
    """Actor object to start running simulation on workers.
    Gradient computation is also executed on this object."""

    def __init__(self, metaAgentID):
        self.metaAgentID = metaAgentID
        self.device = torch.device('cuda') if USE_GPU else torch.device('cpu')
        self.localNetwork = AttentionNet(INPUT_DIM, EMBEDDING_DIM)
        self.localNetwork.to(self.device)

    def get_weights(self):
        return self.localNetwork.state_dict()

    def set_weights(self, weights):
        self.localNetwork.load_state_dict(weights)

    def singleThreadedJob(self, episodeNumber, budget_range, sample_length, num_plants):
        save_img = True if (SAVE_IMG_GAP != 0 and episodeNumber % SAVE_IMG_GAP == 0) else False
        worker = Worker(self.metaAgentID, self.localNetwork, episodeNumber, budget_range, sample_length, num_plants, self.device, save_image=save_img, greedy=False)
        worker.work(episodeNumber)

        jobResults = worker.experience
        perf_metrics = worker.perf_metrics
        return jobResults, perf_metrics

    def job(self, global_weights, episodeNumber, budget_range, sample_length=None, num_plants = None):
        print("starting episode {} on metaAgent {}".format(episodeNumber, self.metaAgentID))
        # set the local weights to the global weight values from the master network
        self.set_weights(global_weights)

        jobResults, metrics = self.singleThreadedJob(episodeNumber, budget_range, sample_length, num_plants)

        info = {
            "id": self.metaAgentID,
            "episode_number": episodeNumber,
        }

        return jobResults, metrics, info

  
@ray.remote(num_cpus=1, num_gpus=len(CUDA_DEVICE)/NUM_META_AGENT)
class RLRunner(Runner):
    def __init__(self, metaAgentID):        
        super().__init__(metaAgentID)


if __name__=='__main__':
    ray.init()
    runner = RLRunner.remote(0)
    job_id = runner.singleThreadedJob.remote(1)
    out = ray.get(job_id)
    print(out[1])