Fix DQN bug (wrong target Q-value for illegal actions) #1259
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Came across a bug in the Pytorch DQN implementation in
open_spiel/python/pytorch/dqn.py
.TLDR: I replaced
sys.float_info.min
withtorch.finfo(torch.float).min
which is the minimum value of a float32.This code computes the max Q-target, setting illegal actions' Q-values to a large negative value so that they cannot be considered in the max:
However
ILLEGAL_ACTION_LOGITS_PENALTY
is set tosys.float_info.min
which (surprisingly) is a positive number very close to 0 (see https://docs.python.org/3/library/sys.html#sys.float_info.min).The Tensorflow DQN implementation in
open_spiel/python/algorithms/dqn.py
is correct though:ILLEGAL_ACTION_LOGITS_PENALTY = -1e9
I ran a DQN best response against a Phantom Tic-Tac-Toe PPO policy and got a pretty significant difference of 0.1 in exploitability (consistant across several seeds):
blue: tensorflow DQN, orange: torch DQN before fix, green: torch DQN after fix