def game(mode):
torch.set_num_threads(1)
env = EnvFindGoals()
max_epi_iter = 1500
max_MC_iter = 200
agent = COMA(N_action=5)
train_curve = []
warm_up_run = 200
obs1_norm = RunningMeanStd()
obs2_norm = RunningMeanStd()
reward_rms1 = RunningMeanStd()
reward_rms2 = RunningMeanStd()
discounted_reward1 = RewardForwardFilter(gamma=0.999)
discounted_reward2 = RewardForwardFilter(gamma=0.999)
env.reset()
o1_list = []
o2_list = []
#Normalize Obs for each agent
for i in range(warm_up_run):
obs1 = env.get_agt1_obs()
obs2 = env.get_agt2_obs()
o1_list.append(obs1)
o2_list.append(obs2)
obs1_norm.update(o1_list)
obs2_norm.update(o2_list)
for epi_iter in range(max_epi_iter):
env.reset()
o1_list = []
a1_list = []
pi_a1_list = []
o2_list = []
a2_list = []
pi_a2_list = []
r_list = []
r1_int_list = []
r2_int_list = []
total_int_rwd1_list = []
total_int_rwd2_list = []
adv1_int = []
adv2_int = []
dones_list = []
v1_list, v2_list = [], []
acc_r = 0
for MC_iter in range(max_MC_iter):
#env.render()
obs1 = env.get_agt1_obs()
obs2 = env.get_agt2_obs()
o1_list.append(obs1)
o2_list.append(obs2)
#print("obs1 =",obs1)
#print("intrinsic reward =",agent.compute_intrinsic_reward1(obs1))
action1, pi_a1, action2, pi_a2, v1, v2 = agent.get_action(
((obs1 - obs1_norm.mean) / obs1_norm.var).clip(-5, 5),
((obs2 - obs2_norm.mean) / obs2_norm.var).clip(-5, 5))
int_rwd1 = agent.compute_intrinsic_reward1(
((obs1 - obs1_norm.mean) / obs1_norm.var).clip(-5, 5))
int_rwd2 = agent.compute_intrinsic_reward2(
((obs2 - obs2_norm.mean) / obs2_norm.var).clip(-5, 5))
#print("int_rwd1 =",int_rwd1)
#print("int_rwd2 =",int_rwd2)
v1_list.append(v1.data.numpy()[0])
v2_list.append(v2.data.numpy()[0])
#print(v1.data.numpy()[0])
r1_int_list.append(int_rwd1)
r2_int_list.append(int_rwd2)
a1_list.append(action1)
pi_a1_list.append(pi_a1)
a2_list.append(action2)
pi_a2_list.append(pi_a2)
[reward_1, reward_2], done = env.step([action1, action2])
#print(reward_1, reward_2)
if done == False:
dones_list.append(0)
else:
dones_list.append(1)
acc_r = acc_r + reward_1
r_list.append(reward_1)
###Test using reward_2 instead
#acc_r = acc_r + reward_2
#r_list.append(reward_2)
if done:
break
obs1_norm.update(o1_list)
obs2_norm.update(o2_list)
"""
for i in reversed(r1_int_list):
r1_int_temp = discounted_reward1.update(i)
for j in reversed(r2_int_list):
r2_int_temp = discounted_reward2.update(j)
"""
#print("mean =",reward_rms1.mean)
mean1, std1, count1 = np.mean(r1_int_list), np.std(r1_int_list), len(
r1_int_list)
mean2, std2, count2 = np.mean(r2_int_list), np.std(r2_int_list), len(
r2_int_list)
#mean1, std1, count1 = np.mean(r1_int_temp), np.std(r1_int_temp), len(r1_int_temp)
#mean2, std2, count2 = np.mean(r2_int_temp), np.std(r2_int_temp), len(r2_int_temp)
reward_rms1.update_from_moments(mean1, std1**2, count1)
reward_rms2.update_from_moments(mean2, std2**2, count2)
#print("var =",reward_rms1.var)
#adv1_int = (r1_int_list-reward_rms1.mean)/np.sqrt(reward_rms1.var)
#adv2_int = (r2_int_list-reward_rms2.mean)/np.sqrt(reward_rms2.var)
#May be not correct way of approx advantages
total_int_rwd1_list = r1_int_list / np.sqrt(reward_rms1.var)
total_int_rwd2_list = r2_int_list / np.sqrt(reward_rms2.var)
dones_list = np.stack(np.expand_dims(dones_list, axis=1)).transpose()
total_int_rwd1_list = np.stack(total_int_rwd1_list).transpose()
total_int_rwd2_list = np.stack(total_int_rwd2_list).transpose()
v1_list = np.stack(v1_list).transpose()
v2_list = np.stack(v2_list).transpose()
#print(len(total_int_rwd1_list),len(dones_list),len(v1_list))
#target_int1, adv1_int = make_train_data_me(total_int_rwd1_list,dones_list,v1_list,0.999,MC_iter)
#target_int2, adv2_int = make_train_data_me(total_int_rwd2_list,dones_list,v2_list,0.999,MC_iter)
#print(target_int1,adv1_int)
if mode == 'intrinsic':
adv1_int = total_int_rwd1_list - reward_rms1.mean / np.sqrt(
reward_rms1.var)
adv2_int = total_int_rwd2_list - reward_rms2.mean / np.sqrt(
reward_rms2.var)
else:
adv1_int = 0
adv2_int = 0
#print("adv1_int=",adv1_int)
if epi_iter % 1 == 0:
train_curve.append(acc_r / MC_iter)
print('Episode', epi_iter, 'reward', acc_r / MC_iter)
agent.train(o1_list, a1_list, pi_a1_list, o2_list, a2_list, pi_a2_list,
r_list, adv1_int, adv2_int)
#plt.plot(train_curve, linewidth=1, label='COMA')
#plt.show()
env.reset()
return train_curve
from env_FindGoals import EnvFindGoals
import random
if __name__ == '__main__':
env = EnvFindGoals()
max_iter = 10000
for i in range(max_iter):
print("iter= ", i)
action_list = [random.randint(0, 4), random.randint(0, 4)]
reward_list, done = env.step(action_list)
print(env.agt1_pos, env.agt2_pos)
env.render()
env.reset()
o1_list = []
a1_list = []
pi_a1_list = []
o2_list = []
a2_list = []
pi_a2_list = []
r_list = []
acc_r = 0
for MC_iter in range(max_MC_iter):
# env.render()
obs1 = env.get_agt1_obs()
obs2 = env.get_agt2_obs()
o1_list.append(obs1)
o2_list.append(obs2)
action1, pi_a1, action2, pi_a2 = agent.get_action(obs1, obs2)
a1_list.append(action1)
pi_a1_list.append(pi_a1)
a2_list.append(action2)
pi_a2_list.append(pi_a2)
[reward_1, reward_2], done = env.step([action1, action2])
acc_r = acc_r + reward_1
r_list.append(reward_1)
if done:
break
if epi_iter % 10 == 0:
train_curve.append(acc_r/MC_iter)
print('Episode', epi_iter, 'reward', acc_r/MC_iter)
agent.train(o1_list, a1_list, pi_a1_list, o2_list, a2_list, pi_a2_list, r_list)
plt.plot(train_curve, linewidth=1, label='COMA')
plt.show()
def game(mode):
torch.set_num_threads(1)
env = EnvFindGoals()
max_epi_iter = 1101
max_MC_iter = 200
N_action = 5
agent = COMA(N_action=5)
train_curve = []
warm_up_run = 100
obs1_norm = RunningMeanStd()
obs2_norm = RunningMeanStd()
reward_rms1 = RunningMeanStd()
reward_rms2 = RunningMeanStd()
discounted_reward1 = RewardForwardFilter(gamma=0.999)
discounted_reward2 = RewardForwardFilter(gamma=0.999)
env.reset()
o1_list = []
o2_list = []
intrisic_surprise_loss = nn.MSELoss()
loss_intrisic_rms = RunningMeanStd()
#Normalize Obs for each agent
for i in range(warm_up_run):
############ agent1 move and agent2 standstill
obs1 = env.get_agt1_obs()
obs2 = env.get_agt2_obs()
env.reset()
for _ in range(1000):
action1 = np.random.randint(0, N_action)
action2 = 4 #np.random.randint(0,N_action)
[reward_1, reward_2], done = env.step([action1, action2])
obs_all = env.get_full_obs()
next_obs = agent.get_next_state(obs_all, action1, "1")
#print("next_obs=",next_obs.shape)
#print("_",img_to_tensor(obs_all).unsqueeze(0).detach().shape)
loss1 = agent.pretrain_loss1(
img_to_tensor(obs_all).unsqueeze(0).detach(), next_obs)
agent.pretrain_train(loss1, "1")
#print("loss1",loss1)
env.reset()
for _ in range(1000):
action1 = 4 #np.random.randint(0,N_action)
action2 = np.random.randint(0, N_action)
[reward_1, reward_2], done = env.step([action1, action2])
obs_all = env.get_full_obs()
next_obs = agent.get_next_state(obs_all, action2, "2")
#print("next_obs=",next_obs.shape)
#print("_",img_to_tensor(obs_all).unsqueeze(0).detach().shape)
loss2 = agent.pretrain_loss2(
img_to_tensor(obs_all).unsqueeze(0).detach(), next_obs)
agent.pretrain_train(loss2, "2")
#print("loss2",loss2)
print("Pre-training is ", i * 100 / (warm_up_run), "percent complete")
o1_list.append(obs1)
o2_list.append(obs2)
obs1_norm.update(o1_list)
obs2_norm.update(o2_list)
#print("obs1=",obs1)
#print("obs2=",obs2)
for epi_iter in range(max_epi_iter):
env.reset()
o1_list = []
a1_list = []
pi_a1_list = []
o2_list = []
a2_list = []
pi_a2_list = []
r_list = []
r1_int_list = []
r2_int_list = []
total_int_rwd1_list = []
total_int_rwd2_list = []
adv1_int = []
adv2_int = []
dones_list = []
v1_list, v2_list = [], []
acc_r = 0
obs_all_s = 0
pred_s_list = []
pred_s_dat_list = []
pre_train_state_list = []
loss_intrisic_surprise_list = []
for MC_iter in range(max_MC_iter):
#env.render()
obs1 = env.get_agt1_obs()
obs2 = env.get_agt2_obs()
o1_list.append(obs1)
o2_list.append(obs2)
obs_all_s = env.get_full_obs()
#print("obs_all_s =",obs_all_s.shape)
#print("intrinsic reward =",agent.compute_intrinsic_reward1(obs1))
action1, pi_a1, action2, pi_a2, v1, v2 = agent.get_action(
((obs1 - obs1_norm.mean) / obs1_norm.var).clip(-5, 5),
((obs2 - obs2_norm.mean) / obs2_norm.var).clip(-5, 5))
pred_next_state = agent.get_next_all_state(obs_all_s, action1,
action2)
pre_train_next_state = agent.get_next_state(
obs_all_s, action1,
"1") #agent.pretrain_agent1.forward1(obs_all_s,action1)
#print("pre_train_next_state",pre_train_next_state.shape)
pre_train_next_state2 = agent.pretrain_agent2.forward2(
pre_train_next_state, action2)
pre_train_state_list.append(pre_train_next_state2)
#print("pred_next_state =",pred_next_state.shape)
pred_s_list.append(pred_next_state)
int_rwd1 = agent.compute_intrinsic_reward1(
((obs1 - obs1_norm.mean) / obs1_norm.var).clip(-5, 5))
int_rwd2 = agent.compute_intrinsic_reward2(
((obs2 - obs2_norm.mean) / obs2_norm.var).clip(-5, 5))
intrisic_surprise = intrisic_surprise_loss(
pred_next_state, pre_train_next_state2).data.numpy()
loss_intrisic_surprise_list.append([intrisic_surprise])
#print("intrisic_surprise=",intrisic_surprise)
#print("int_rwd1 =",int_rwd1)
#print("int_rwd2 =",int_rwd2)
v1_list.append(v1.data.numpy()[0])
v2_list.append(v2.data.numpy()[0])
#print(v1.data.numpy()[0])
r1_int_list.append(int_rwd1)
r2_int_list.append(int_rwd2)
a1_list.append(action1)
pi_a1_list.append(pi_a1)
a2_list.append(action2)
pi_a2_list.append(pi_a2)
[reward_1, reward_2], done = env.step([action1, action2])
obs_all_s = env.get_full_obs()
obs_all_s = img_to_tensor(obs_all).unsqueeze(0).detach()
#print("obs_all_s =",obs_all_s.shape)
pred_s_dat_list.append(obs_all_s)
#print(reward_1, reward_2)
if done == False:
dones_list.append(0)
else:
dones_list.append(1)
sum_r = reward_1 + reward_2
if reward_2 > 0 or reward_1 > 0:
print("reward_1=", reward_1)
print("reward_2=", reward_2)
print("MC_iter", MC_iter)
r_list.append(sum_r)
###Test using reward_2 instead
#acc_r = acc_r + reward_2
#r_list.append(reward_2)
acc_r = acc_r + sum_r
if done:
break
print("acc_r=", acc_r)
obs1_norm.update(o1_list)
obs2_norm.update(o2_list)
loss_intrisic_rms.update(loss_intrisic_surprise_list)
"""
for i in reversed(r1_int_list):
r1_int_temp = discounted_reward1.update(i)
for j in reversed(r2_int_list):
r2_int_temp = discounted_reward2.update(j)
"""
#print("mean =",reward_rms1.mean)
mean1, std1, count1 = np.mean(r1_int_list), np.std(r1_int_list), len(
r1_int_list)
mean2, std2, count2 = np.mean(r2_int_list), np.std(r2_int_list), len(
r2_int_list)
#mean1, std1, count1 = np.mean(r1_int_temp), np.std(r1_int_temp), len(r1_int_temp)
#mean2, std2, count2 = np.mean(r2_int_temp), np.std(r2_int_temp), len(r2_int_temp)
reward_rms1.update_from_moments(mean1, std1**2, count1)
reward_rms2.update_from_moments(mean2, std2**2, count2)
#print("var =",reward_rms1.var)
#adv1_int = (r1_int_list-reward_rms1.mean)/np.sqrt(reward_rms1.var)
#adv2_int = (r2_int_list-reward_rms2.mean)/np.sqrt(reward_rms2.var)
#May be not correct way of approx advantages
total_int_rwd1_list = r1_int_list / np.sqrt(reward_rms1.var)
total_int_rwd2_list = r2_int_list / np.sqrt(reward_rms2.var)
dones_list = np.stack(np.expand_dims(dones_list, axis=1)).transpose()
total_int_rwd1_list = np.stack(total_int_rwd1_list).transpose()
total_int_rwd2_list = np.stack(total_int_rwd2_list).transpose()
v1_list = np.stack(v1_list).transpose()
v2_list = np.stack(v2_list).transpose()
#print(len(total_int_rwd1_list),len(dones_list),len(v1_list))
#target_int1, adv1_int = make_train_data_me(total_int_rwd1_list,dones_list,v1_list,0.999,MC_iter)
#target_int2, adv2_int = make_train_data_me(total_int_rwd2_list,dones_list,v2_list,0.999,MC_iter)
#print(target_int1,adv1_int)
if mode == 'intrinsic':
adv1_int = np.zeros(
(len(r1_int_list), 1)
) #(r1_int_list)/np.sqrt(reward_rms1.var)#total_int_rwd1_list - reward_rms1.mean/np.sqrt(reward_rms1.var)
adv2_int = np.zeros(
(len(r1_int_list), 1)
) #(r2_int_list)/np.sqrt(reward_rms2.var)#total_int_rwd2_list - reward_rms2.mean/np.sqrt(reward_rms2.var)
adv_sur_int = loss_intrisic_surprise_list / np.sqrt(
loss_intrisic_rms.var)
else:
adv1_int = np.zeros((len(r1_int_list), 1))
adv2_int = np.zeros((len(r2_int_list), 1))
adv_sur_int = np.zeros((len(r2_int_list), 1))
#print("adv1_int=",adv1_int)
if epi_iter % 1 == 0:
train_curve.append(acc_r)
print("####################################")
print('Episode', epi_iter, 'reward', acc_r)
print("####################################")
agent.train(o1_list, a1_list, pi_a1_list, o2_list, a2_list, pi_a2_list,
r_list, adv1_int, adv2_int, pred_s_list, pred_s_dat_list,
adv_sur_int)
#plt.plot(train_curve, linewidth=1, label='COMA')
#plt.show()
env.reset()
return train_curve
env = EnvFindGoals()
action_dim = 5
state_dim = 2
agent = SoftQ(state_dim=2, action_dim=5)
max_MC_iter = 200
max_epi_iter = 500
batch_size = 64
replay_buffer = ReplayBuffer(10000)
train_curve = []
for epi in range(max_epi_iter):
env.reset()
acc_reward = 0
for MC_iter in range(max_MC_iter):
# print("MC= ", MC_iter)
env.render()
state = np.array(env.agt1_pos).reshape((1, 2))
action1 = agent.get_action(state)
# print(action1)
reward_list, done = env.step([action1, 4])
acc_reward = acc_reward + reward_list[0]
next_state = np.array(env.agt1_pos).reshape((1, 2))
replay_buffer.push(state, action1, reward_list[0], next_state,
done)
if len(replay_buffer) > batch_size:
agent.train(replay_buffer.sample(batch_size))
if done:
break
print('Episode', epi, 'reward', acc_reward / MC_iter)
train_curve.append(acc_reward)
plt.plot(train_curve, linewidth=1, label='SAC')
plt.show()
N_b = 5 # no bigger than N_s
N_action = 5
N_node_list = [10, 10]
max_opt_iter = 100
max_MC_iter = 300
# get obs_dict
obs_list1 = []
obs_list2 = []
env = EnvFindGoals()
print('start')
for j in range(100):
print(j)
env.reset()
for i in range(100):
reward_list, done = env.step([random.randint(0, 4), random.randint(0, 4)])
obs1 = env.get_obs()[0]
obs2 = env.get_obs()[1]
if is_obs_in_list(obs1, obs_list1) == False:
obs_list1.append(obs1)
if is_obs_in_list(obs2, obs_list2) == False:
obs_list2.append(obs2)
print(len(obs_list1))
print(len(obs_list2))
obs_dict = [obs_list1, obs_list2]
print('here')
#obs_dict = [[0], [0], [0]]
env = EnvFindGoals()
optimizer = GDICEopt(N_agent, N_node_list, obs_dict, N_action, N_s, N_b, max_opt_iter, max_MC_iter, env)
#optimizer.load_model('GDICE_model_FindGoals')