def worker(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden
global dim_input_hidden, dim_hidden_output
global sigma
global env
#Local:
seed = int(input_worker[0])
p = input_worker[1]
env.seed(seed)
initial_positions = [[
0.99944425, -0.03333454, 0.99606243, -0.08865463, -0.0176822,
0.05332311
], [
0.99990637, 0.01368383, 0.99981907, 0.01902162, 0.02523626, -0.02548034
],
[
9.99192425e-01, 4.01808159e-02, 9.98170503e-01,
6.04619401e-02, 2.84129862e-02, 2.68182438e-04
],
[
0.99940723, 0.0344266, 0.99632547, 0.08564789,
-0.04982971, 0.07554994
],
[
9.99516909e-01, -3.10797289e-02, 9.99999914e-01,
4.15201486e-04, -3.08654909e-02, -4.65136501e-02
]]
np.random.shuffle(initial_positions)
initial_observation = initial_positions[0]
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden, numOutput)
NN.wi = p[0]
NN.wo = p[1]
#distortions
epsilon_wo = np.random.multivariate_normal(
[0 for x in range(dim_hidden_output)],
np.identity(dim_hidden_output)).reshape((numHidden, numOutput))
epsilon_wi = np.random.multivariate_normal(
[0 for x in range(dim_input_hidden)],
np.identity(dim_input_hidden)).reshape((numInput, numHidden))
#parameters update
NN.wo = NN.wo + epsilon_wo * sigma #remark:we should merge the two, and reshape the matrix
NN.wi = NN.wi + epsilon_wi * sigma
#initial_observation=env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=250)
return (reward_worker, epsilon_wi, epsilon_wo)
def worker(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden
global dim_hidden_output, dim_hidden_output
global sigma
global env
#Local:
seed = int(input_worker[0])
p = input_worker[1]
env.seed(seed)
np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden, numOutput)
NN.wi = p[0]
NN.wo = p[1]
#distortions
epsilon = np.random.multivariate_normal(
np.zeros(dim_hidden_output + dim_input_hidden),
np.identity(dim_hidden_output + dim_input_hidden))
epsilon_wo = epsilon[0:dim_hidden_output].reshape((numHidden, numOutput))
epsilon_wi = epsilon[dim_hidden_output:dim_hidden_output +
dim_input_hidden + 1].reshape((numInput, numHidden))
#epsilon_wo = np.random.multivariate_normal([0 for x in range(dim_hidden_output)],np.identity(dim_hidden_output)).reshape((numHidden,numOutput))
#epsilon_wi = np.random.multivariate_normal([0 for x in range(dim_input_hidden)],np.identity(dim_input_hidden)).reshape((numInput,numHidden))
#parameters update
NN.wo = NN.wo + epsilon_wo * sigma #remark:we should merge the two, and reshape the matrix
NN.wi = NN.wi + epsilon_wi * sigma
#initial_observation=env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=250)
return (reward_worker, epsilon_wi, epsilon_wo)
plt.plot([x[0] for x in reward_episode_wo_GS_SR_1])
plt.plot([x[0] for x in reward_episode_wo_GS_SR_2])
plt.plot([x[0] for x in reward_episode_wo_GS_SR_3])
params_GS_1=load_obj('params_GS_semi')
params_GS_2=load_obj('params_GS_semi_robust(2)')
params_GS_3=load_obj('params_GS_semi_robust2(2)')
params_wo_GS_1=load_obj('params_woGS_semi(2)')
params_wo_GS_2=load_obj('params_woGS_semi_robust(2)')
params_wo_GS_3=load_obj('params_woGS_semi_robust2(2)')
env, obs_dim , act_dim=initGym()
NN_GS_1=NeuralNetwork(obs_dim,8,act_dim)
NN_GS_2=NeuralNetwork(obs_dim,8,act_dim)
NN_GS_3=NeuralNetwork(obs_dim,8,act_dim)
simulation_reward_GS=[]
NN_GS_3.wi=params_GS_3[0]
NN_GS_3.wo=params_GS_3[1]
NN_GS_1.wi=params_GS_1[0]
NN_GS_1.wo=params_GS_1[1]
NN_GS_2.wi=params_GS_2[0]
NN_GS_2.wo=params_GS_2[1]
[np.mean(reward_workers),
np.median(reward_workers)])
index_sort = np.argsort(reward_workers)
reward_workers = np.sort(reward_workers)
#fitness = fitness_shaping_paper(reward_workers)
print(reward_workers)
epsilon_wi = [epsilon_wi[i] for i in index_sort]
epsilon_wo = [epsilon_wo[i] for i in index_sort]
"""
#grad1:
params[0] = params[0] - alpha*(1/(num_workers*sigma))*sum([eps*F*w for eps,F,w in zip(epsilon_wi,reward_workers,fitness)])
params[1] = params[1] - alpha*(1/(num_workers*sigma))*sum([eps*F*w for eps,F,w in zip(epsilon_wo,reward_workers,fitness)])
"""
#grad1:
params[0] = params[0] + alpha * (1 / (num_workers * sigma)) * sum(
[eps * F for eps, F in zip(epsilon_wi, reward_workers)])
params[1] = params[1] + alpha * (1 / (num_workers * sigma)) * sum(
[eps * F for eps, F in zip(epsilon_wo, reward_workers)])
print(reward_episode[-1][0])
print(reward_episode)
#%%
plt.plot([x[0] for x in reward_episode])
save_obj(params, 'params-v2')
### Test:
NN = NeuralNetwork(numInput, numHidden, numOutput)
NN.wi = params[0]
NN.wo = params[1]
runNN(NN, env)