You know what I’m going to try both approaches on a simple environment and see which one learns faster! I think sequence number can be fixed with pad_sequance.



 raharth:

DQN, DDQN, Policy Gradient?


I’m using A3C for this. I’d created the “fancy linear version” by adding GRU cell without feeding back hidden states from last step :). It gave much better results than A3C alone but still not the level I want!!
here’s the model I’m upgrading:
  

      github.com
  

  
    ayjabri/refresh/blob/master/09_A3C_GRU.py 17


    #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Attempting to solve Atari games using A2C

@author: Ayman Jabri
"""
import os
import torch
import ptan
from lib import data, utils, mp_utils, model
from tensorboardX import SummaryWriter
import torch.multiprocessing as mp


ALGORITHM = 'A3C_GRU'
FORKS = 4
MINI_BATCH = 16





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