SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 4: Details of FamO2O implemented on IQL [24] for the D4RL Locomotion and AntMaze benchmark [8]. Except for our newly added balance model, the hyperparameters of our method keep the same as those of the vanilla IQL.
Name	Value
Balance model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	5 (Locomotion) / 1 (AntMaze)
hidden units	[256, 256]
activation	ReLU [1]
Universal model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
Q function model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
target Q soft update rate	5 × 10-3
V function model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
target V soft update rate	5 × 10-3
quantile	0.7 (Locomotion) / 0.9 (AntMaze)
Other training parameters	batch size	256
replay buffer size	2 × 106
discount	0.99
Table 5: Details of FamO2O implemented on AWAC [35] for the D4RL Locomotion and AntMaze benchmark [8]. Except for our newly added balance model, the hyperparameters of our method keep the same as those of the vanilla AWAC.
Name	Value
Balance model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	5
hidden units	[256, 256]
activation	ReLU [1]
Universal model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256, 256, 256]
activation	ReLU [1]
weight decay	1 × 104
Q function model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
target Q soft update rate	5 × 10-3
Other training parameters	batch size	1024
replay buffer size	2 × 106
discount	0.99
Table 6: Details of FamO2O implemented on CQL [27] for the D4RL Locomotion benchmark [8]. Except for our newly added balance model, the hyperparameters of our method keep the same as those of the vanilla AWAC.
Name	Value
Balance model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
Universal model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
Q function model	optimizer	Adam [21]
learning rate	3 × 10-4
update frequency	1
hidden units	[256, 256]
activation	ReLU [1]
target Q soft update rate	5 × 10-3
Other training parameters	batch size	256
replay buffer size	2 × 106
discount	0.99