I am trying to train a number of decentralized DQN policies on the Battle environment. Any observation size other than 42 x 42 or 84 x 84 (default supported sizes) with my own conv_filters does not work if the number of filters is more than one. for example:
"model": {
"dim": 15,
"conv_filters": [
[32, [5, 5], 1],
[64, [5, 5], 1],
[128, [5, 5], 1],
[256, [3, 3], 1]
],
},
does not work, but
"model": {
"dim": 15,
"conv_filters": [
[32, [15, 15], 1]
],
},
does. I am using torch which ends up calling visionnet.py, so in principle, there shouldn’t be a problem, but apparently, there is … Any idea what the problem might be ?