Commit 60507984 authored by Alexis Brenon's avatar Alexis Brenon
Browse files

馃敡 Use random network as starting network

Try to use a random network as starting network with real data to see
the impact of the pre-train phase.
parent cfdeb0fe
......@@ -21,25 +21,25 @@ local args = {
-- Output
output = {
path = paths.concat("outputs", os.date("%FT%T")),
log_level = 10,
save_freq = 200000,
log_level = 20,
save_freq = 100e3,
},
-- Environments
training_environment = {
class = "smarthome.sweethome.GraphicalAnnotatedSweetHome",
class = "smarthome.sweethome.GraphicalSensorSweetHome",
params = {
map_path = paths.concat("assets", "domus_inferred.svg"),
max_tries = 1,
deterministic = true,
map_path = paths.concat("assets", "domus.svg"),
data_path = paths.concat("data", "SweetHomeRaw"),
history_length = 3,
},
},
testing_environment = {
class = "smarthome.sweethome.GraphicalAnnotatedSweetHome",
class = "smarthome.sweethome.GraphicalSensorSweetHome",
params = {
map_path = paths.concat("assets", "domus_inferred.svg"),
max_tries = 1,
deterministic = true,
map_path = paths.concat("assets", "domus.svg"),
data_path = paths.concat("data", "SweetHomeRaw-test"),
history_length = 3,
},
},
......@@ -47,58 +47,32 @@ local args = {
agent = {
class = "NeuralQLearner",
params = {
-- Networks
preprocess = {
class = "Downsample",
params = {
scale_size = {84,84}
},
params = {},
},
inference = {
class = "Inference",
params = {
input_size = {1, 84, 84},
conv_layers = {
{
n_filters = 32,
field_size = {width = 8, height = 8},
stride = {width = 4, height = 4},
zero_padding = {width = 0, height = 0}
},
{
n_filters = 64,
field_size = {width = 4, height = 4},
stride = {width = 2, height = 2},
zero_padding = {width = 0, height = 0}
},
{
n_filters = 64,
field_size = {width = 3, height = 3},
stride = {width = 1, height = 1},
zero_padding = {width = 0, height = 0}
}
}
},
params = {},
},
-- Experience pool params
memory = {
pool_size = 100,
history_length = 3,
},
learn_start = 50,
target_q = 100,
update_freq = 1,
minibatch_size = 32,
ep_start = 1,
ep_end = 0.5,
ep_endt = 100000,
}
-- update_freq = 12,
-- minibatch_size = 32,
--target_q = 1024,
},
},
-- Experiment
experiment = {
class = "BaseExperiment",
params = {
steps = 300e3,
eval_freq = 5e3,
steps = 1e6,
eval_freq = 10e3,
eval_steps = 5000,
},
},
......
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