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add unperturbed trial with ranking

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Connor Olding 2017-09-07 19:00:09 +00:00
parent 9ce1f87ade
commit ddc8cae4c6
1 changed files with 31 additions and 11 deletions
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40
main.lua
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@ -41,6 +41,7 @@ local consider_past_rewards = false
local learn_start_select = false local learn_start_select = false
-- --
local epoch_trials = 40 local epoch_trials = 40
local unperturbed_trial = true -- do a trial without any noise.
local learning_rate = 0.3 -- bigger now that i'm shaping trials etc. local learning_rate = 0.3 -- bigger now that i'm shaping trials etc.
local deviation = 0.05 local deviation = 0.05
-- --
@ -75,7 +76,7 @@ local bad_states = {
local epoch_i = 0 local epoch_i = 0
local base_params local base_params
local trial_i = 0 local trial_i = -1 -- NOTE: trial 0 is an unperturbed trial, if enabled.
local trial_noise = {} local trial_noise = {}
local trial_rewards = {} local trial_rewards = {}
local trials_remaining = 0 local trials_remaining = 0
@ -535,17 +536,25 @@ local function prepare_epoch()
for j = 1, #base_params do noise[j] = nn.normal() end for j = 1, #base_params do noise[j] = nn.normal() end
trial_noise[i] = noise trial_noise[i] = noise
end end
trial_i = 0 trial_i = -1
end end
local function load_next_trial() local function load_next_trial()
trial_i = trial_i + 1 trial_i = trial_i + 1
print('loading trial', trial_i)
local W = nn.copy(base_params) local W = nn.copy(base_params)
local noise = trial_noise[trial_i] local noise = trial_noise[trial_i]
local devsqrt = sqrt(deviation) local devsqrt = sqrt(deviation)
if trial_i == 0 and not unperturbed_trial then
trial_i = 1
end
if trial_i > 0 then
print('loading trial', trial_i)
local noise = trial_noise[trial_i]
for i, v in ipairs(base_params) do for i, v in ipairs(base_params) do
W[i] = v + devsqrt * noise[i] W[i] = v + deviation * noise[i]
end
else
print("test trial")
end end
network:distribute(W) network:distribute(W)
end end
@ -574,6 +583,17 @@ local function fitness_shaping(rewards)
return shaped_returns return shaped_returns
end end
local function unperturbed_rank(rewards, unperturbed_reward)
-- lifted from: https://github.com/atgambardella/pytorch-es/blob/master/train.py
local nth_place = 1
for i, v in ipairs(rewards) do
if v > unperturbed_reward then
nth_place = nth_place + 1
end
end
return nth_place
end
local function learn_from_epoch() local function learn_from_epoch()
print() print()
print('rewards:', trial_rewards) print('rewards:', trial_rewards)
@ -582,12 +602,12 @@ local function learn_from_epoch()
insert(all_rewards, v) insert(all_rewards, v)
end end
if consider_past_rewards then if unperturbed_trial then
normalize_wrt(trial_rewards, all_rewards) local nth_place = unperturbed_rank(trial_rewards, trial_rewards[0])
else
normalize(trial_rewards) -- a rank of 1 means our gradient is uninformative.
print(("test trial: %d out of %d"):format(nth_place, #trial_rewards))
end end
--print('normalized:', trial_rewards)
local step = nn.zeros(#base_params) local step = nn.zeros(#base_params)
local shaped_rewards = fitness_shaping(trial_rewards) local shaped_rewards = fitness_shaping(trial_rewards)
@ -625,7 +645,7 @@ local function do_reset()
if state == 'dead' and get_timer() == 0 then state = 'timeup' end if state == 'dead' and get_timer() == 0 then state = 'timeup' end
print("resetting in state: "..state..". reward:", reward) print("resetting in state: "..state..". reward:", reward)
if trial_i > 0 then trial_rewards[trial_i] = reward end if trial_i >= 0 then trial_rewards[trial_i] = reward end
if epoch_i == 0 or trial_i == epoch_trials then if epoch_i == 0 or trial_i == epoch_trials then
if epoch_i > 0 then learn_from_epoch() end if epoch_i > 0 then learn_from_epoch() end