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if __name__ == '__main__':
  gin.parse_config(dqn_config, skip_unknown=False)

  # train our runner
  dqn_runner = run_experiment.create_runner(DQN_PATH, schedule='continuous_train')
  print('Will train DQN agent, please be patient, may be a while...')
  dqn_runner.run_experiment()
  print('Done training!')

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from absl import app
from dopamine.utils import example_viz_lib

def main(_):
  example_viz_lib.run(agent='dqn',
                      game='Pong',
                      num_steps=100,
                      root_dir='C:/Users/Holm/Documents/dopamine',
                      restore_ckpt='C:/Users/Holm/Documents/dopamine/models/tf_ckpt-7',
                      use_legacy_checkpoint=False)

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# import libraries
import os
import matplotlib.pyplot as plt
import torch
import torchvision
from torch.nn import functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import Trainer
from multiprocessing import Process

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import torch
import torch.nn as nn

from models.ConvLSTMCell import ConvLSTMCell

class EncoderDecoderConvLSTM(nn.Module):
    def __init__(self, nf, in_chan):
        super(EncoderDecoderConvLSTM, self).__init__()

        """ ARCHITECTURE 

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import torch.nn as nn
import torch


class ConvLSTMCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size, bias):
        """
        Initialize ConvLSTM cell.

holmdk

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