notwa/smbot
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

tweaks

Browse Source
This commit is contained in:
Connor Olding 2018-05-02 13:06:28 +02:00
parent 2bdd67b721
commit 7831f534c9
2 changed files with 23 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
config.lua
View File

@ -12,22 +12,22 @@ local cfg = {
init_zeros = true, -- instead of he_normal noise or whatever. init_zeros = true, -- instead of he_normal noise or whatever.
frameskip = 4, frameskip = 4,
-- true greedy epsilon has both deterministic and det_epsilon set. -- true greedy epsilon has both deterministic and det_epsilon set.
deterministic = true, -- use argmax on outputs instead of random sampling. deterministic = false, -- use argmax on outputs instead of random sampling.
det_epsilon = false, -- take random actions with probability eps. det_epsilon = false, -- take random actions with probability eps.
graycode = false, graycode = false,
epoch_trials = 50, epoch_trials = 5,
epoch_top_trials = 25, -- new with ARS. epoch_top_trials = 2, -- new with ARS.
unperturbed_trial = true, -- do a trial without any noise. unperturbed_trial = false, -- do a trial without any noise.
negate_trials = true, -- try pairs of normal and negated noise directions. negate_trials = true, -- try pairs of normal and negated noise directions.
time_inputs = true, -- binary inputs of global frame count time_inputs = true, -- binary inputs of global frame count
-- ^ note that this now doubles the effective trials. -- ^ note that this now doubles the effective trials.
deviation = 0.05, --0.075 --0.1 deviation = 0.32,
--learning_rate = 0.01 / approx_cossim(7051) --learning_rate = 0.01 / approx_cossim(7051)
learning_rate = 1.0, learning_rate = 0.32,
--learning_rate = 0.0032 / approx_cossim(66573) --learning_rate = 0.0032 / approx_cossim(66573)
--learning_rate = 0.0056 / approx_cossim(66573) --learning_rate = 0.0056 / approx_cossim(66573)
weight_decay = 0.00032, --0.001 --0.0023 weight_decay = 0.0032,
cap_time = 200, --400 cap_time = 200, --400
timer_loser = 1/2, timer_loser = 1/2,
@ -36,6 +36,9 @@ local cfg = {
playback_mode = false, playback_mode = false,
} }
-- TODO: so, uhh..
-- what happens when playback_mode is true but unperturbed_trial is false?
cfg.epoch_top_trials = math.min(cfg.epoch_trials, cfg.epoch_top_trials) cfg.epoch_top_trials = math.min(cfg.epoch_trials, cfg.epoch_top_trials)
cfg.eps_start = 1.0 * cfg.frameskip / 64 cfg.eps_start = 1.0 * cfg.frameskip / 64

15
main.lua
View File

@ -436,14 +436,16 @@ local function prepare_epoch()
-- but that's a fair tradeoff for dividing memory used by noise by `epoch_trials`. -- but that's a fair tradeoff for dividing memory used by noise by `epoch_trials`.
local precision = (pow(cfg.deviation, 1/-0.51175585) - 8.68297257) / 1.66484392 local precision = (pow(cfg.deviation, 1/-0.51175585) - 8.68297257) / 1.66484392
if cfg.graycode then
print(("chosen precision: %.2f"):format(precision)) print(("chosen precision: %.2f"):format(precision))
end
for i = 1, cfg.epoch_trials do for i = 1, cfg.epoch_trials do
local noise = nn.zeros(#base_params) local noise = nn.zeros(#base_params)
-- NOTE: change in implementation: deviation is multiplied here -- NOTE: change in implementation: deviation is multiplied here
-- and ONLY here now. -- and ONLY here now.
if i % 2 == 0 then -- FIXME: just messing around. --if i % 2 == 0 then -- FIXME: just messing around.
--if cfg.graycode then if cfg.graycode then
--local precision = 1 / cfg.deviation --local precision = 1 / cfg.deviation
--print(cfg.deviation, precision) --print(cfg.deviation, precision)
for j = 1, #base_params do for j = 1, #base_params do
@ -598,7 +600,14 @@ local function learn_from_epoch()
top_rewards[sind + 0] = trial_rewards[sind + 0] top_rewards[sind + 0] = trial_rewards[sind + 0]
top_rewards[sind + 1] = trial_rewards[sind + 1] top_rewards[sind + 1] = trial_rewards[sind + 1]
end end
print("top:", top_rewards) --print("top:", top_rewards)
local delta_rewards = {} -- only used for printing.
for i, ind in ipairs(indices) do
local sind = (ind - 1) * 2 + 1
delta_rewards[i] = abs(top_rewards[sind + 0] - top_rewards[sind + 1])
end
print("best deltas:", delta_rewards)
local _, reward_dev = calc_mean_dev(top_rewards) local _, reward_dev = calc_mean_dev(top_rewards)
--print("mean, dev:", _, reward_dev) --print("mean, dev:", _, reward_dev)