def main(env_name):
"""
Run the gym test using the specified environment
:param env_name: Name of the Unity environment binary to launch
"""
env = UnityEnv(env_name, worker_id=1, use_visual=False, no_graphics=True)
try:
# Examine environment parameters
print(str(env))
# Reset the environment
initial_observations = env.reset()
if len(env.observation_space.shape) == 1:
# Examine the initial vector observation
print("Agent observations look like: \n{}".format(
initial_observations))
for _episode in range(10):
env.reset()
done = False
episode_rewards = 0
while not done:
actions = env.action_space.sample()
obs, reward, done, _ = env.step(actions)
episode_rewards += reward
print("Total reward this episode: {}".format(episode_rewards))
finally:
env.close()
class Chaser_v1(Environment):
unity_env_worker_id = 0
def __init__(self, platform):
if platform == OSName.MAC:
env_filename = EnvironmentName.CHASER_V1_MAC.value
elif platform == OSName.WINDOWS:
env_filename = EnvironmentName.CHASER_V1_WINDOWS.value
else:
env_filename = None
self.env = UnityEnv(environment_filename=env_filename,
worker_id=Chaser_v1.unity_env_worker_id,
use_visual=True,
multiagent=True).unwrapped
self.increase_env_worker_id()
super(Chaser_v1, self).__init__()
self.action_shape = self.get_action_shape()
self.state_shape = self.get_state_shape()
self.cnn_input_height = self.state_shape[0]
self.cnn_input_width = self.state_shape[1]
self.cnn_input_channels = self.state_shape[2]
self.observation_space = self.env.observation_space
self.continuous = True
@staticmethod
def increase_env_worker_id():
Chaser_v1.unity_env_worker_id += 1
def get_n_states(self):
n_states = 3
return n_states
def get_n_actions(self):
n_actions = 3
return n_actions
def get_state_shape(self):
return self.env.observation_space.shape
def get_action_shape(self):
return self.env.action_space.shape
def reset(self):
state = self.env.reset()
return state
def step(self, action):
next_state, reward, done, info = self.env.step(action)
adjusted_reward = reward
return next_state, reward, adjusted_reward, done, info
def close(self):
self.env.close()
def worker(id, td3_trainer, rewards_queue, replay_buffer, max_episodes, max_steps, batch_size, explore_steps, \
update_itr, explore_noise_scale, eval_noise_scale, reward_scale, DETERMINISTIC, hidden_dim, model_path):
'''
the function for sampling with multi-processing
'''
print(td3_trainer, replay_buffer)
env_name="./tac_follow_new"
env = UnityEnv(env_name, worker_id=id+15, use_visual=False, use_both=True)
# training loop
for eps in range(max_episodes):
frame_idx=0
rewards=[]
episode_reward = 0
state, info = env.reset()
# state=state[:6]
for step in range(max_steps):
if frame_idx > explore_steps:
action = td3_trainer.policy_net.get_action(state, deterministic = DETERMINISTIC, explore_noise_scale=explore_noise_scale)
else:
action = td3_trainer.policy_net.sample_action()
try:
next_state, reward, done, info = env.step(action)
# next_state = next_state[:6]
except KeyboardInterrupt:
print('Finished')
td3_trainer.save_model(model_path)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
frame_idx += 1
# if len(replay_buffer) > batch_size:
if replay_buffer.get_length() > batch_size:
for i in range(update_itr):
_=td3_trainer.update(batch_size, deterministic=DETERMINISTIC, eval_noise_scale=eval_noise_scale, reward_scale=reward_scale)
if eps % 10 == 0 and eps>0:
# plot(rewards, id)
td3_trainer.save_model(model_path)
if done:
break
print('Episode: ', eps, '| Episode Reward: ', episode_reward)
if len(rewards) == 0: rewards.append(episode_reward)
else: rewards.append(rewards[-1]*0.9+episode_reward*0.1)
rewards_queue.put(episode_reward)
td3_trainer.save_model(model_path)
def test_multi_agent(mock_env):
mock_brain = create_mock_group_spec()
mock_braininfo = create_mock_vector_step_result(num_agents=2)
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
with pytest.raises(UnityGymException):
UnityEnv(" ", multiagent=False)
env = UnityEnv(" ", use_visual=False, multiagent=True)
assert isinstance(env.reset(), list)
actions = [env.action_space.sample() for i in range(env.number_agents)]
obs, rew, done, info = env.step(actions)
assert isinstance(obs, list)
assert isinstance(rew, list)
assert isinstance(done, list)
def test_closing(env_name):
"""
Run the gym test and closes the environment multiple times
:param env_name: Name of the Unity environment binary to launch
"""
try:
env1 = UnityEnv(env_name,
worker_id=1,
use_visual=False,
no_graphics=True)
env1.close()
env1 = UnityEnv(env_name,
worker_id=1,
use_visual=False,
no_graphics=True)
env2 = UnityEnv(env_name,
worker_id=2,
use_visual=False,
no_graphics=True)
env2.reset()
finally:
env1.close()
env2.close()
def test_gym_wrapper(mock_env):
mock_brain = create_mock_group_spec()
mock_braininfo = create_mock_vector_step_result()
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = UnityEnv(" ", use_visual=False, multiagent=False)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert env.observation_space.contains(obs)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, (bool, np.bool_))
def test_gym_wrapper(mock_env):
mock_brain = create_mock_brainparams()
mock_braininfo = create_mock_vector_braininfo()
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = UnityEnv(" ", use_visual=False, multiagent=False)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
class UnityEnvWrapper(gym.Env):
def __init__(self, env_config):
self.vector_index = env_config.vector_index
self.worker_index = env_config.worker_index
self.worker_id = env_config["unity_worker_id"] + env_config.worker_index
# Name of the Unity environment binary to launch
env_name = '/home/jim/projects/unity_ray/basic_env_linux/basic_env_linux'
self.env = UnityEnv(env_name, worker_id=self.worker_id, use_visual=False, multiagent=False, no_graphics=True) #
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
def reset(self):
return self.env.reset()
def step(self, action):
return self.env.step(action)
def test_multi_agent(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0,
stack=False,
num_agents=2)
# Test for incorrect number of agents.
with pytest.raises(UnityGymException):
UnityEnv(' ', multiagent=False)
env = UnityEnv(' ', use_visual=False, multiagent=True)
assert isinstance(env.reset(), list)
actions = [env.action_space.sample() for i in range(env.number_agents)]
obs, rew, done, info = env.step(actions)
assert isinstance(obs, list)
assert isinstance(rew, list)
assert isinstance(done, list)
assert isinstance(info, dict)
def test_gym_wrapper_visual(mock_env, use_uint8):
mock_spec = create_mock_group_spec(number_visual_observations=1)
mock_decision_step, mock_terminal_step = create_mock_vector_steps(
mock_spec, number_visual_observations=1)
setup_mock_unityenvironment(mock_env, mock_spec, mock_decision_step,
mock_terminal_step)
env = UnityEnv(" ", use_visual=True, uint8_visual=use_uint8)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert env.observation_space.contains(obs)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, (bool, np.bool_))
assert isinstance(info, dict)
def test_gym_wrapper(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0,
stack=False,
num_agents=1)
# Test for incorrect number of agents.
with pytest.raises(UnityGymException):
UnityEnv(' ', use_visual=False, multiagent=True)
env = UnityEnv(' ', use_visual=False)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
class TEST():
def __init__(self, n_episodes, env_name, model):
# Nª Episodes
self.n_episodes = n_episodes
# Environment
self.env_name = env_name
channel = EngineConfigurationChannel()
self.env = UnityEnv(self.env_name,
worker_id=0,
use_visual=False,
side_channels=[channel],
no_graphics=False,
multiagent=False)
self.action_size, self.state_size = Utils.getActionStateSize(self.env)
# Model
self.model = ActorCritic(self.state_size, self.action_size,
seed=0).to(device)
# Initialize time step (for updating every "update_every" time steps)
self.t_step = 1
# Start test
self.load_model(model)
self.test()
def test(self):
# Initial observation
env_info = self.env.reset()
state = env_info
# Data
self.data = Data(1, 100)
# Episodes done
n_done = 0
# Test loop
while n_done <= self.n_episodes:
# Action of agent
action, value = self.act(state)
# Send the action to the environment
next_state, reward, done, info = self.env.step(action)
# Update t_step
self.t_step += 1
# Update n_done
if done:
n_done += 1
# Next state
state = next_state
# Update the score
reward_ = np.expand_dims(reward, axis=0)
value_ = value.unsqueeze(0)
done_ = np.expand_dims(done, axis=0)
self.data.update_score(reward_, value_, done_, self.t_step)
# Summary
if done:
self.data.summary(self.t_step)
def load_model(self, model):
self.model.load_state_dict(torch.load(model))
def act(self, state):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Get actions probabilities and value from ActorCritic model
self.model.eval()
with torch.no_grad():
action_probs, value = self.model(state)
self.model.train()
prob = F.softmax(action_probs, -1)
# Get action and log of probabilities
action = prob.multinomial(num_samples=1)
return action, value
env = UnityEnv(environment_filename=env_name,
worker_id=0,
use_visual=False,
multiagent=True)
print(str(env))
state_size = env.observation_space.shape[0]
'''if (env.observation_space.shape[2]==3):
plt.imshow(env.observation_space[0, :, :, :])'''
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)
# agent.load("./save/cartpole-ddqn.h5")//
done = False
batch_size = 332
for e in range(EPISODES):
state = env.reset()
#print (state.shape)
state = np.reshape(state, [3, state_size, state_size])
print("_____________state size______")
print(state_size)
for time in range(500):
# env.render()
#action = agent.act(state)
actionlst = []
i = 0
while (i < 32):
action = agent.act(state)
actionlst.append(action)
i = i + 1
next_state, reward, done, _ = env.step(action)
def cartpole():
env = UnityEnv(environment_filename=ENV_NAME,
worker_id=2,
use_visual=False,
multiagent=True)
score_logger = ScoreLogger(ENV_NAME)
agents_brain = []
agents_action = []
index_list = []
agents_alive = []
count = 0
count1 = 0
num_agents = env.number_agents
print("___________Number of agents in cartpole __")
print(num_agents)
observation_space = env.observation_space.shape[0]
action_space = env.action_space.n
dqn_solver = DQNSolver(observation_space, action_space)
print("__dqn solver______")
print(dqn_solver)
#model = tf.keras.models.load_model("")
for x in range((env.number_agents)):
agents_brain.append(dqn_solver)
print("______agentbrain____")
print(agents_brain)
print("_Agent action___")
print(agents_action)
learning_brain = copy.deepcopy(agents_brain)
run = 0
state = env.reset()
initialstate = copy.deepcopy(state)
while True:
run += 1
env.reset()
print("____________STATE____________-")
print(state[0])
state = copy.deepcopy(initialstate)
agents_brain = []
agents_action = []
index_list = []
agents_alive = []
count = 0
count1 = 0
num_agents = int(state[0][-5])
agents_brain = copy.deepcopy(learning_brain)
print(learning_brain)
print(agents_brain)
print(state)
#for x in range ( (env.number_agents - 1) ):
step = 0
while True:
step += 1
env.render()
print("___________STatte Lenth_______")
print(len(state))
print("______selffish___")
print(state[0])
agents_action = [1] * len(state)
copied_agents_alive = copy.deepcopy(agents_alive)
print("__________numagents_____")
for x in range(num_agents - 1):
state[x] = np.reshape(state[x], [1, observation_space])
agents_action[x] = agents_brain[x].act(state[x])
print(agents_action)
state_next, reward, terminal, info = env.step(
agents_action, num_agents)
print("_______Reward________")
print(reward)
print("_____________NEXT STATE LENGTH____________")
print(len(state_next))
if (len(state_next) == 0):
break
agents_alive = state_next[0][-13:-5]
num_agents = int(state_next[0][-5])
print("_______num agnets in cartpole________")
print(num_agents)
print("_____index list")
print(index_list)
print(agents_alive)
agents_alive1 = np.delete(agents_alive, index_list)
print("_______Alive agent list_______")
print(agents_alive1)
flag = False
# del agents_alive[index_list[x]]
for x in range(len(agents_alive)):
if (agents_alive[x] == float(1)):
for y in range(len(index_list)):
if (index_list[y] == x):
flag = True
if (flag == False):
index_list.append(x)
flag = False
index_to_remove = []
for x in range(len(agents_alive1)):
if (agents_alive1[x] == float(1)):
learning_brain[index_list[count]] = agents_brain[x]
index_to_remove.append(x)
count = count + 1
agents_brain = [
i for j, i in enumerate(agents_brain)
if j not in index_to_remove
]
print("____________AGENTS_BRAIN_________")
print(len(agents_brain))
print("_______________Terminal_____________")
print(terminal)
if (terminal[0] == True):
print("Run: " + str(run) + ", exploration: " +
str(dqn_solver.exploration_rate) + ", score: " +
str(step))
score_logger.add_score(step, run)
for x in range(len(copied_agents_alive)):
learning_brain[x] = agents_brain[count1]
count1 = count1 + 1
for x in range(len(learning_brain)):
learning_brain[x].save(str(run) + "brain" + str(x) + ".h5")
break
for x in range(num_agents - 1):
state[x] = np.reshape(state[x], [1, observation_space])
state_next[x] = np.reshape(state_next[x],
[1, observation_space])
agents_brain[x].remember(state[x], agents_action[x], reward[x],
state_next[x], terminal[x])
agents_brain[x].experience_replay()
state = state_next
class Worker(object):
def __init__(self, wid):
self.wid = wid
self.env = UnityEnv(env_name,
worker_id=wid,
use_visual=True,
use_both=True)
# self.env=Reacher(render=True)
self.ppo = GLOBAL_PPO
self.pins_x = []
self.pins_y = []
def ImgProcess(self, img, Done=False):
cimg, edge_detected_image, contour_centers = image_processing(img)
# cimg = large_circle_detect(cimg, edge_detected_image) # this consumes most time
cimg, VALID_DETECT = contour_center_check(contour_centers,
cimg,
NUM_PINS=NUM_PINS)
# cv2.imwrite(save_path+str(filename),cimg)
contour_centers = CenterRegister(contour_centers)
if VALID_DETECT: # pins detection correct
reshape_contour_centers = np.array(contour_centers).transpose()
self.pins_x.append(reshape_contour_centers[0])
self.pins_y.append(reshape_contour_centers[1])
reshape_pins_x = np.array(self.pins_x).transpose()
reshape_pins_y = np.array(self.pins_y).transpose()
displacement_pins_x = self.pins_x[-1] - self.pins_x[0]
displacement_pins_y = self.pins_y[-1] - self.pins_y[0]
plt.figure(1)
for i in range(NUM_PINS):
plt.subplot(211)
plt.plot(np.arange(len(self.pins_x)), reshape_pins_x[i])
plt.title('Position')
plt.subplot(212)
plt.plot(np.arange(len(self.pins_x)),
reshape_pins_x[i] - reshape_pins_x[i][0])
plt.title('Displacement')
plt.tight_layout()
plt.savefig('./ppo_pins.png')
if Done:
plt.clf()
# return pins position x, y for current frame, displacement of pins position x,y
return self.pins_x[-1], self.pins_y[
-1], displacement_pins_x, displacement_pins_y
def work(self):
global GLOBAL_EP, GLOBAL_RUNNING_R, GLOBAL_UPDATE_COUNTER
step_set = []
epr_set = []
step = 0
while not COORD.should_stop():
s, info = self.env.reset()
''' image processing '''
img = (s[:, :, 0] * 255).astype(np.uint8)
try:
pins_x, pins_y, pins_dis_x, pins_dis_y = self.ImgProcess(
img, Done=False)
except:
print('Image Processing Error!')
s = np.concatenate((pins_dis_x, pins_dis_y))
''''''
# vector_s = info["brain_info"].vector_observations[0, :] # get the vector observation
# s=vector_s
# print(s.shape, info["brain_info"].vector_observations[0, :])
step += 1
ep_r = 0
buffer_s, buffer_a, buffer_r = [], [], []
self.pins_x = []
self.pins_y = []
for t in range(EP_LEN):
if not ROLLING_EVENT.is_set(): # while global PPO is updating
ROLLING_EVENT.wait() # wait until PPO is updated
buffer_s, buffer_a, buffer_r = [], [], [
] # clear history buffer, use new policy to collect data
a = self.ppo.choose_action(s)
s_, r, done, info = self.env.step(a)
''' implementation of plot version one, deprecated '''
# plt.imshow(s_[:,:,0])
# # plt.show()
# plt.savefig('./img256_test/tac_test'+str(step)+str(t)+'.png')
'''
image size of plt is not exactly the original array size, but with axis etc;
therefore use Image--imlementation of plot version two
'''
# im = Image.fromarray((s_[:,:,0] * 255).astype(np.uint8))
# im.save('./img256f_r30/tac'+str(step)+str(t)+'.png')
''' image processing '''
img = (s_[:, :, 0] * 255).astype(np.uint8)
if t > EP_LEN - 1:
Done = True
else:
Done = False
try:
pins_x, pins_y, pins_dis_x, pins_dis_y = self.ImgProcess(
img, Done)
except:
print('Image Processing Error!')
s_ = np.concatenate((pins_dis_x, pins_dis_y))
''' get the vector observation '''
# vector_s = info["brain_info"].vector_observations[0, :] # get the vector observation
# s_=vector_s
# print('a: ',a) # shape: []
# print('s: ',s_) # shape: []
# plt.imshow(s[:,:,0])
# plt.show()
# print('r: ',r) # shape: scalar
# print('done: ', done) # shape: True/False
# s=s.reshape(-1) # convert from 3D to 1D
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(
(r + 8) / 8) # normalize reward, find to be useful
s = s_
ep_r += r
GLOBAL_UPDATE_COUNTER += 1 # count to minimum batch size, no need to wait other workers
if t == EP_LEN - 1 or GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
v_s_ = self.ppo.get_v(s_)
discounted_r = [] # compute discounted reward
for r in buffer_r[::-1]:
v_s_ = r + GAMMA * v_s_
discounted_r.append(v_s_)
discounted_r.reverse()
bs, ba, br = np.vstack(buffer_s), np.vstack(
buffer_a), np.array(discounted_r)[:, np.newaxis]
buffer_s, buffer_a, buffer_r = [], [], []
QUEUE.put(np.hstack((bs, ba, br))) # put data in the queue
if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
ROLLING_EVENT.clear() # stop collecting data
UPDATE_EVENT.set() # globalPPO update
if GLOBAL_EP >= EP_MAX: # stop training
COORD.request_stop()
break
# record reward changes, plot later
if len(GLOBAL_RUNNING_R) == 0: GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(GLOBAL_RUNNING_R[-1] * 0.9 +
ep_r * 0.1)
GLOBAL_EP += 1
print(
'{0:.1f}%'.format(GLOBAL_EP / EP_MAX * 100),
'|W%i' % self.wid,
'|Ep_r: %.2f' % ep_r,
)
step_set.append(step)
# print(step)
epr_set.append(ep_r)
K.set_session(sess)
#Setting up the env
#TODO Worker_id can be changed to run in parallell
#Flatten_branched gives us a onehot encoding of all 54 action combinations.
print("Opening unity env")
env = UnityEnv(
"../unity_envs/kais_banana_with_explicit_charge_decision_red_battery_300_timesteps",
worker_id=22,
use_visual=True,
flatten_branched=True,
seed=seed
) #KOE: Note: If I accept images as uint8_visual=True, I have to convert to float later.
print("Resetting env")
initial_observation = env.reset()
#KOETODO This would have to be manually configured for each environment.
battery = 100 # [Health]
prev_battery = battery
# game.get_available_buttons_size() # [Turn Left, Turn Right, Move Forward]
print("Action space is: ", env.action_space)
action_size = env.action_space.n
print("Env has ", action_size, " actions.")
measurement_size = 3 # [Battery, posion, food]
timesteps = [1, 2, 4, 8, 16, 32] # For long horizon: [4,8,16,32,64,128]
goal_size = measurement_size * len(timesteps)
img_rows, img_cols = 84, 84 #KOE: Think this is still correct.
# Convert image into Black and white
class PPO():
def __init__(self):
# Hyperparameters
self.learning_rate = 0.0003
self.betas = (0.9, 0.999)
self.gamma = 0.99
self.eps_clip = 0.2
self.buffer_size = 2048
self.batch_size = 256
self.K_epochs = 3
self.max_steps = 100000
self.tau = 0.95
self.entropy_coef = 0.001
self.value_loss_coef = 0.5
self.summary_freq = 1000
# Environment
self.env_name = "Environments/env1/Unity Environment"
channel = EngineConfigurationChannel()
self.env = UnityEnv(self.env_name,
worker_id=0,
use_visual=False,
side_channels=[channel],
no_graphics=False,
multiagent=True)
channel.set_configuration_parameters(time_scale=100)
self.action_size, self.state_size = Utils.getActionStateSize(self.env)
self.n_agents = self.env.number_agents
print("Nº of Agents: ", self.n_agents)
# Model
self.model = ActorCritic(self.state_size, self.action_size,
seed=0).to(device)
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate,
betas=self.betas)
self.MseLoss = nn.MSELoss()
# Buffer memory
self.memory = []
for _ in range(self.n_agents):
self.memory.append(Buffer())
# Initialize time step (for updating when buffer_size is full)
self.t_step = 1
def train(self):
# Initial observation
env_info = self.env.reset()
state = env_info
# Data
self.data = Data(self.n_agents, self.summary_freq)
# Training loop
for _ in range(self.max_steps):
action = []
logprobs = []
value = []
# Action of agent
for i in range(self.n_agents):
a, b, c = self.act(state[i])
action.append(a)
logprobs.append(b)
value.append(c)
# Send the action to the environment
next_state, reward, done, info = self.env.step(action)
# Done
done_ = []
for i in range(self.n_agents):
done_.append(1 - done[i])
# Agent step
for i in range(self.n_agents):
self.step(state[i], action[i], reward[i], next_state[i],
done_[i], logprobs[i], value[i], self.memory[i])
# Update t_step
self.t_step += 1
# Next state
state = next_state
# Update the score
self.data.update_score(reward, value, done, self.t_step)
# Summary
if self.t_step % self.summary_freq == 0:
self.data.summary(self.t_step)
# Save
self.save()
def save(self):
torch.save(self.model.state_dict(), 'Saved Models/model.pth')
self.data.results()
def load_model(self, model):
self.model.load_state_dict(torch.load(model))
def act(self, state):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Get actions probabilities and value from ActorCritic model
self.model.eval()
with torch.no_grad():
action_probs, value = self.model(state)
self.model.train()
prob = F.softmax(action_probs, -1)
log_probs = F.log_softmax(action_probs, -1)
# Get action and log of probabilities
action = prob.multinomial(num_samples=1)
log_probs = log_probs.gather(1, action)
return action, log_probs, value
def step(self, state, action, reward, next_state, done, logprobs, value,
memory):
# Update model when buffer_size is full
if memory.len_() == (self.buffer_size / self.n_agents):
self.learn()
for i in range(self.n_agents):
self.memory[i].reset()
# Save experience in buffer memory
memory.add(state, action, reward, next_state, done, logprobs, value)
def evaluate(self, states, next_states, actions, rewards, masks,
compute_gae):
logits, values = self.model(states)
probs = F.softmax(logits, -1)
log_probs = F.log_softmax(logits, -1)
entropies = -(log_probs * probs).sum(1, keepdim=True)
log_probs = log_probs.gather(1, actions.unsqueeze(1))
values_ = values
_, value = self.model(next_states)
values = torch.cat((values, value.data))
returns = []
if (compute_gae):
gae = torch.zeros(1, 1)
for i in reversed(range(len(rewards))):
# Generalized Advantage Estimation
delta_t = rewards[i] + self.gamma * masks[i] * values[
i + 1].data - values[i].data
gae = gae * self.gamma * self.tau * masks[i] + delta_t
returns.insert(0, gae + values[i])
return log_probs, values_, entropies, returns
def compute_returns(self):
returns_ = []
for i in range(self.n_agents):
# Get Experiences (of each agent)
experiences = self.memory[i].get()
states, actions, rewards, next_states, dones, logprobs_, values_ = experiences
# Evaluate
_, _, _, r = self.evaluate(states,
next_states,
actions,
rewards,
dones,
compute_gae=True)
returns_.append(r)
l = []
for i in range(len(returns_)):
for j in range(len(returns_[0])):
l.append(returns_[i][j])
return l
def learn(self):
# Get Experiences
states, actions, rewards, next_states, dones, logprobs_, values_ = self.getExp(
)
returns_eval = self.compute_returns()
returns_eval = torch.tensor(returns_eval).to(device)
returns_eval = returns_eval.unsqueeze(1)
# Optimize policy for K epochs:
for _ in range(self.K_epochs):
# List with all indices
l = np.arange(self.buffer_size)
l = list(l)
x = self.buffer_size // self.batch_size
for _ in range(x):
# Take a random batch
indices = random.sample(l, self.batch_size)
old_logprobs = torch.empty(self.batch_size, 1)
old_values = torch.empty(self.batch_size, 1)
old_actions = torch.empty(self.batch_size)
old_states = torch.empty(self.batch_size, self.state_size)
old_next_states = torch.empty(self.batch_size, self.state_size)
old_rewards = np.zeros(self.batch_size)
returns = torch.empty(self.batch_size, 1)
for i in range(len(indices)):
old_logprobs[i] = logprobs_[indices[i]]
old_values[i] = values_[indices[i]]
old_actions[i] = actions[indices[i]]
old_states[i] = states[indices[i]]
old_next_states[i] = next_states[indices[i]]
old_rewards[i] = rewards[indices[i]]
returns[i] = returns_eval[indices[i]]
old_actions = old_actions.long()
# Remove indices to not repeat
for i in indices:
l.remove(i)
# Evaluate
logprobs, state_values, dist_entropy, _ = self.evaluate(
old_states,
old_next_states,
old_actions,
rewards,
dones,
compute_gae=False)
# Finding the ratio (pi_theta / pi_theta__old):
ratios = torch.exp(logprobs - old_logprobs)
# Finding Surrogate Loss:
advantages = returns - old_values
surr1 = ratios * advantages
surr2 = torch.clamp(ratios, 1 - self.eps_clip,
1 + self.eps_clip) * advantages
# LOSS = ACTOR LOSS + CRITIC_DISCOUNT * CRITIC_LOSS - ENTROPY_BETA * ENTROPY
loss = -torch.min(
surr1, surr2) + self.value_loss_coef * self.MseLoss(
state_values,
returns) - self.entropy_coef * dist_entropy
# Optimizer step
self.optimizerStep(self.optimizer, loss.mean())
def optimizerStep(self, optimizer, loss):
optimizer.zero_grad()
loss.backward()
optimizer.step()
def getExp(self):
states, actions, rewards, next_states, dones, logprobs, values = [], [], [], [], [], [], []
for i in range(self.n_agents):
experiences = self.memory[i].get()
states.append(experiences[0])
actions.append(experiences[1])
rewards.append(experiences[2])
next_states.append(experiences[3])
dones.append(experiences[4])
logprobs.append(experiences[5])
values.append(experiences[6])
states_, actions_, rewards_, next_states_, dones_, logprobs_, values_ = [], [], [], [], [], [], []
for i in range(len(states)):
for j in range(len(states[0])):
states_.append(states[i][j])
actions_.append(actions[i][j])
rewards_.append(rewards[i][j])
next_states_.append(next_states[i][j])
dones_.append(dones[i][j])
logprobs_.append(logprobs[i][j])
values_.append(values[i][j])
states__ = torch.empty(self.buffer_size, self.state_size)
actions__ = torch.empty(self.buffer_size)
next_states__ = torch.empty(self.buffer_size, self.state_size)
dones__ = torch.empty(self.buffer_size)
logprobs__ = torch.empty(self.buffer_size, 1, 1)
values__ = torch.empty(self.buffer_size)
for i in range(self.buffer_size):
states__[i] = states_[i]
actions__[i] = actions_[i]
next_states__[i] = next_states_[i]
dones__[i] = dones_[i]
logprobs__[i] = logprobs_[i]
values__[i] = values_[i]
return states__, actions__, rewards_, next_states__, dones__, logprobs__, values__
class AC():
def __init__(self):
# Hyperparameters
self.learning_rate = 0.0003
self.gamma = 0.99
self.batch_size = 256
self.max_steps = 100000
self.tau = 0.95
self.entropy_coef = 0.001
self.value_loss_coef = 0.5
self.summary_freq = 1000
# Environment
self.env_name = "Environments/env1/Unity Environment"
channel = EngineConfigurationChannel()
self.env = UnityEnv(self.env_name,
worker_id=0,
use_visual=False,
side_channels=[channel],
no_graphics=False,
multiagent=False)
channel.set_configuration_parameters(time_scale=100)
self.action_size, self.state_size = Utils.getActionStateSize(self.env)
self.n_agents = self.env.number_agents
# Model
self.model = ActorCritic(self.state_size, self.action_size,
seed=0).to(device)
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate)
# Buffer memory
self.memory = Buffer()
# Initialize time step (for updating every "batch_size" time steps)
self.t_step = 1
def train(self):
# Initial observation
env_info = self.env.reset()
state = env_info
# Data
self.data = Data(self.n_agents, self.summary_freq)
# Training loop
for _ in range(self.max_steps):
# Action of agent
action, value = self.act(state)
# Send the action to the environment
next_state, reward, done, info = self.env.step(action)
# Agent step
self.step(state, action, reward, next_state, done)
# Update t_step
self.t_step += 1
# Next state
state = next_state
# Update the score
reward_ = np.expand_dims(reward, axis=0)
value_ = value.unsqueeze(0)
done_ = np.expand_dims(done, axis=0)
self.data.update_score(reward_, value_, done_, self.t_step)
# Summary
if self.t_step % self.summary_freq == 0:
self.data.summary(self.t_step)
# Save
self.save()
def save(self):
torch.save(self.model.state_dict(), 'Saved Models/model.pth')
self.data.results()
def load_model(self, model):
self.model.load_state_dict(torch.load(model))
def act(self, state):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Get actions probabilities and value from ActorCritic model
self.model.eval()
with torch.no_grad():
action_probs, value = self.model(state)
self.model.train()
prob = F.softmax(action_probs, -1)
# Get action and log of probabilities
action = prob.multinomial(num_samples=1)
return action, value
def step(self, state, action, reward, next_state, done):
# Save experience in buffer memory
self.memory.add(state, action, reward, next_state, done)
# Learn every "batch_size" time steps
if self.t_step % self.batch_size == 0:
experiences = self.memory.get()
self.learn(experiences)
self.memory.reset()
def learn(self, experiences):
# Get Experiences
states, actions, rewards, next_states = experiences
logits, values = self.model(states)
probs = F.softmax(logits, -1)
log_probs = F.log_softmax(logits, -1)
entropies = -(log_probs * probs).sum(1, keepdim=True)
log_probs = log_probs.gather(1, actions.unsqueeze(1))
_, value = self.model(next_states)
values = torch.cat((values, value.data))
policy_loss = 0
value_loss = 0
R = values[-1]
gae = torch.zeros(1, 1)
for i in reversed(range(len(rewards))):
R = self.gamma * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimation
delta_t = rewards[i] + self.gamma * values[i +
1].data - values[i].data
gae = gae * self.gamma * self.tau + delta_t
policy_loss = policy_loss - (log_probs[i] * gae) - (
self.entropy_coef * entropies[i])
# Loss
loss = (policy_loss + self.value_loss_coef * value_loss)
# Optimizer step
self.optimizerStep(self.optimizer, loss)
def optimizerStep(self, optimizer, loss):
optimizer.zero_grad()
loss.backward()
optimizer.step()
from tensorflow.python import keras
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.optimizers import Adam
#from tensorflow.python.keras._impl.keras.optimizers import Adam
from tensorflow.python.layers.core import Dense
import matplotlib.pyplot as plt
import sys
from gym_unity.envs import UnityEnv
multi_env_name = "D:/ml-agents-0.8.0/UnitySDK/A.exe"
multi_env = UnityEnv(multi_env_name, worker_id=1,
use_visual=False, multiagent=True)
# Examine environment parameters
print(str(multi_env))
# Reset the environment
initial_observations = multi_env.reset()
if len(multi_env.observation_space.shape) == 1:
# Examine the initial vector observation
print("Agent observations look like: \n{}".format(initial_observations[0]))
else:
# Examine the initial visual observation
print("Agent observations look like:")
if multi_env.observation_space.shape[2] == 3:
plt.imshow(initial_observations[0][:,:,:])
else:
plt.imshow(initial_observations[0][:,:,0])
for episode in range(10):
initial_observation = multi_env.reset()
target_weights[i] = weights[i] * self.tau + target_weights[i] * (1 - self.tau)
self.target_model.set_weights(target_weights)
def save_model(self, fn):
self.model.save(fn)
env_name = "../env/GridWorld.x86_64" # Name of the Unity environment binary to launch
env = UnityEnv(env_name, worker_id=0, use_visual=True)
episodes = 2000
# updateTargetNetwork = 1000
dqn_agent = DQN(env=env)
steps = []
env.reset()
for ep in range(episodes):
cur_state = np.array([env.reset()])
done = False
ep_reward = 0
n_steps = 0
while not done:
action = dqn_agent.act(cur_state)
new_state, reward, done, info = env.step(action)
new_state = np.array([new_state])
ep_reward += reward
# reward = reward if not done else -20
dqn_agent.remember(cur_state, action, reward, new_state, done)
# Environment name
# Remember to put battle royale environment configuration within the config folder
env_name = "environment/battle-royale-static"
env = UnityEnv(env_name, worker_id=4, use_visual=False, multiagent=True)
print(str(env))
# ## Examine Observation Space
# In[3]:
# Examine observation space
observation = env.observation_space
env.reset()
print("Agent observation space type: {}".format(observation))
# ## Examine Action Space
# In[4]:
# Examine action space
action = env.action_space
print("Agent action space type: {}".format(action))
# ## Agents Training
# This part shows agent training using MADDPG algoritm
# ### Setup Algorithm Dependencies
def cartpole():
env = UnityEnv(environment_filename=ENV_NAME, worker_id=5, use_visual=False, multiagent = True)
score_logger = ScoreLogger(ENV_NAME)
agents_brain = []
agents_action = []
num_agents = env.number_agents
observation_space = env.observation_space.shape[0]
print("____________Observation_space")
print(observation_space)
action_space = env.action_space.n
dqn_solver = DQNSolver(observation_space, action_space)
for x in range ((env.number_agents)):
agents_brain.append(DQNSolver(observation_space, action_space))
print ("Length of BrainList: ",len(agents_brain))
run = 0
state = env.reset()
print("______INITIAL______")
print(state)
#initialstate = copy.deepcopy(state)
print("*****************************initial state for unity envirmonet**************")
#print(initialstate)
jk = 1
while True:
run += 1
state = env.reset()
#state = copy.deepcopy(initialstate)
num_agents = int(state[0][-5])
print("_____________State _______________")
print(int(state[0][12]))
step = 0
print("################################This is loop################################# :" , jk)
while True:
step += 1
env.render()
agents_action = [1] * len(state)
print(state[0])
print("*******************Length of state******************")
print(len(state))
for x in range(len(state)):
state[x] = np.reshape(state[x], [1, observation_space])
agents_action[x] = agents_brain[int(state[x][0,12]) - 1].act(state[x])
print("Agents Actions List: ",agents_action)
state_next, reward, terminal, info = env.step(agents_action)
#print ("_____________STATE_NEXT___________")
#print (state_next)
if (len(state_next) == 0):
break
agents_alive = state_next[0][-13:-5]
print ("Agents_alive: ", agents_alive)
print ("Rewards: ",reward)
num_agents = int(state_next[0][-5])
print ("Number of agents: ",num_agents)
print("_________Terminal list_______" , terminal)
if (terminal[0] == True):
print("**************************Brain saved******************************")
for x in range(len(agents_brain)):
agents_brain[x].save(str(run) + "brain" + str(x) + ".h5")
jk+=1
print("#####################################Loop is######################## :" , jk)
#break
for x in range(len(state_next)):
state[x] = np.reshape(state[x], [1, observation_space])
state_next[x] = np.reshape(state_next[x], [1, observation_space])
agents_brain[int(state_next[x][0,12]) - 1].remember(state[x], agents_action[x], reward[x], state_next[x], terminal[x])
agents_brain[int(state_next[x][0,12]) - 1].experience_replay()
state = state_next
class Worker(object):
def __init__(self, wid):
self.wid = wid
self.env = UnityEnv(env_name,
worker_id=wid,
use_visual=False,
use_both=True)
# self.env=Reacher(render=True)
self.ppo = GLOBAL_PPO
def work(self):
global GLOBAL_EP, GLOBAL_RUNNING_R, GLOBAL_UPDATE_COUNTER
step_set = []
epr_set = []
step = 0
while not COORD.should_stop():
s, info = self.env.reset()
s = s[:8]
step += 1
ep_r = 0
buffer_s, buffer_a, buffer_r = [], [], []
self.pins_x = []
self.pins_y = []
self.pins_z = []
self.object_x = []
self.object_y = []
self.object_z = []
for t in range(EP_LEN):
if not ROLLING_EVENT.is_set(): # while global PPO is updating
ROLLING_EVENT.wait() # wait until PPO is updated
buffer_s, buffer_a, buffer_r = [], [], [
] # clear history buffer, use new policy to collect data
a = self.ppo.choose_action(s)
s_, r, done, info = self.env.step(a)
# print(np.array(s_).shape)
# plot pins
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r) # normalize reward, find to be useful
pins_x = s_[6::3]
pins_z = s_[8::3]
self.object_x.append(s_[0])
self.object_z.append(s_[2])
self.pins_x.append(pins_x)
self.pins_z.append(pins_z)
relative_x = pins_x - s_[0]
relative_z = pins_z - s_[2]
dis = (relative_x - (self.pins_x[0] - self.object_x[0]))**2 + (
relative_z - (self.pins_z[0] - self.object_z[0]))**2
min_idx = np.argmin(dis)
max_idx = np.argmax(dis)
# add relative position of the pin with smallest deformation
# s_ = np.append(s_[:6], relative_x[min_idx])
# s_ = np.append(s_, relative_z[min_idx])
s_ = np.append(s_[:6], relative_x[max_idx])
s_ = np.append(s_, relative_z[max_idx])
s = s_
ep_r += r
# print('minimal displacement idx: ', min_idx)
GLOBAL_UPDATE_COUNTER += 1 # count to minimum batch size, no need to wait other workers
if t == EP_LEN - 1 or GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
v_s_ = self.ppo.get_v(s_)
discounted_r = [] # compute discounted reward
for r in buffer_r[::-1]:
v_s_ = r + GAMMA * v_s_
discounted_r.append(v_s_)
discounted_r.reverse()
bs, ba, br = np.vstack(buffer_s), np.vstack(
buffer_a), np.array(discounted_r)[:, np.newaxis]
buffer_s, buffer_a, buffer_r = [], [], []
QUEUE.put(np.hstack((bs, ba, br))) # put data in the queue
if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
ROLLING_EVENT.clear() # stop collecting data
UPDATE_EVENT.set() # globalPPO update
if GLOBAL_EP >= EP_MAX: # stop training
COORD.request_stop()
break
if GLOBAL_EP % 50 == 0 and GLOBAL_EP > 0:
self.ppo.save(model_path)
reshape_pins_x = np.array(self.pins_x).transpose()
reshape_pins_z = np.array(self.pins_z).transpose()
plt.clf()
for i in range(NUM_PINS):
plt.subplot(411)
plt.plot(np.arange(len(self.pins_x)), reshape_pins_x[i])
plt.title('X-Position')
plt.subplot(412)
plt.plot(np.arange(len(self.pins_z)), reshape_pins_z[i])
plt.title(
'Y-Position') # although it's z, to match reality, use y
plt.subplot(413)
# plt.plot(np.arange(len(self.pins_x)), reshape_pins_x[i]-self.object_x)
# plt.title('X-Relative')
# plt.plot(np.arange(len(self.pins_x)), reshape_pins_x[i]-reshape_pins_x[i][0])
# plt.title('X-Displacement')
plt.plot(np.arange(len(self.pins_x)),
(reshape_pins_x[i] - self.object_x) -
(reshape_pins_x[i][0] - self.object_x[0]))
plt.title('X-Displacement')
plt.subplot(414)
plt.plot(np.arange(len(self.pins_x)),
(reshape_pins_z[i] - self.object_z) -
(reshape_pins_z[i][0] - self.object_z[0]))
plt.title('Y-Displacement')
plt.xlabel('Time Step')
plt.tight_layout()
plt.savefig('./ppo_pins.png')
# record reward changes, plot later
if len(GLOBAL_RUNNING_R) == 0: GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(GLOBAL_RUNNING_R[-1] * 0.9 +
ep_r * 0.1)
GLOBAL_EP += 1
print(
'{0:.1f}%'.format(GLOBAL_EP / EP_MAX * 100),
'|W%i' % self.wid,
'|Ep_r: %.2f' % ep_r,
)
step_set.append(step)
# print(step)
epr_set.append(ep_r)
if step % 10 == 0: # plot every N episode; some error about main thread for plotting
plt.clf()
plt.plot(step_set, epr_set)
plt.xlabel('Episode')
plt.ylabel('Reward')
try:
plt.savefig('./tac_pins8.png')
except:
print('writing conflict!')
def cartpole():
env = UnityEnv(environment_filename=ENV_NAME,
worker_id=1,
use_visual=False,
multiagent=True)
#score_logger = ScoreLogger(ENV_NAME)
agents_brain = []
agents_action = []
pathname = "C:/HinaProgramm/testingFolder/Unity Environment"
num_agents = env.number_agents
print("Number of agents in enviroment : ", num_agents)
observation_space = env.observation_space.shape[0]
print("____________Observation_space______________")
print(observation_space)
print("__________Action Space________________")
action_space = env.action_space.n
print(action_space)
dqn_solver = DQNSolver(observation_space, action_space)
for x in range((num_agents)):
agents_brain.append(DQNSolver(observation_space, action_space))
print("Length of BrainList: ", len(agents_brain))
run = 0
state = env.reset()
#print("______INITIAL______")
#print(state)
initialstate = copy.deepcopy(state)
#print("*****************************initial state for unity envirmonet**************")
#print(initialstate)
jk = 1
sharecount = 0
eatcount = 0
filecount = 0
#f = str(filecount) + "sahre.csv"
f = open(str(filecount) + "sahre.csv", 'ab')
#J = str(filecount) + "eat.csv"
J = open(str(filecount) + "eat.csv", 'ab')
while True:
run += 1
env.reset()
state = copy.deepcopy(initialstate)
num_agents = int(state[0][-8])
print("_numagents__________", num_agents)
print("_____________State _______________")
print(int(state[0][12]))
step = 0
print(
"################################This is loop################################# :",
jk)
print("_____Run _______ :", run)
while True:
#print("************Number of agents *********")
#print(env.number_agents)
step += 1
env.render()
agents_action = [1] * len(state)
#print(state[0])
#print("*******************Length of state******************")
#print(len(state))
for x in range(len(state)):
state[x] = np.reshape(state[x], [1, observation_space])
agents_action[x] = agents_brain[int(state[x][0, 12]) - 1].act(
state[x])
sharecount += agents_action.count(5)
eatcount += agents_action.count(6)
#print("Agents Actions List: ",agents_action)
state_next, reward, terminal, info = env.step(agents_action)
for x in range(len(agents_action)):
if (agents_action[x] == 5):
new = np.asarray([state_next[x]])
np.savetxt(f, new, delimiter=",")
#f.write(str(state_next[x])+"\r\n")
if (agents_action[x] == 6):
#J.write(str(state_next[x])+"\r\n")
new = np.asarray([state_next[x]])
np.savetxt(J, new, delimiter=",")
print("_____________STATE_NEXT___________")
print(state_next)
if (len(state_next) == 0):
#f.write(str(sharecount))
#J.write(str(eatcount))
#f.close()
#J.close()
filecount += 1
np.savetxt(f, sharecount, delimiter=",")
np.savetxt(J, eatcount, delimiter=",")
break
agents_alive = state_next[0][-16:-8]
print("Agents_alive: ", agents_alive)
print("Rewards: ", reward)
num_agents = int(state_next[0][-8])
print("Number of agents: ", num_agents)
#print("_________Terminal list_______" , terminal)
if (terminal[0] == True):
print(
"**************************Brain saved******************************"
)
for x in range(len(agents_brain)):
agents_brain[x].model.save(pathname + str(run) + "brain" +
str(x) + ".h5")
jk += 1
print(
"#####################################Loop is######################## :",
jk)
#f.write(str(sharecount))
#J.write(str(eatcount))
#f.close()
#J.close()
filecount += 1
break
for x in range(len(state_next)):
state[x] = np.reshape(state[x], [1, observation_space])
state_next[x] = np.reshape(state_next[x],
[1, observation_space])
agents_brain[int(state_next[x][0, 12]) - 1].remember(
state[x], agents_action[x], reward[x], state_next[x],
terminal[x])
agents_brain[int(state_next[x][0, 12]) - 1].experience_replay()
state = state_next
return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
else:
y, adv = 0, []
terminals_reversed = terminal_array[1:][::-1]
for step, dt in enumerate(reversed(x)):
y = dt + gamma * y * (1 - terminals_reversed[step])
adv.append(y)
return np.array(adv)[::-1]
if __name__ == "__main__":
env = UnityEnv('test.app', 0,use_visual=True)
ppo = PPO(env)
all_ep_r = []
t = 0
for ep in range(EP_MAX):
s = env.reset()
ep_r = 0
done = False
while not done:
t+=1
env.render()
a,v = ppo.choose_action(s)
s_, r, done, _ = env.step(a)
ppo.buffer_s.append(s)
ppo.buffer_a.append(a)
ppo.buffer_r.append(r)
ppo.buffer_v.append(v)
ppo.buffer_done.append(done)
s = s_
import numpy as np
import sys
from gym_unity.envs import UnityEnv
env_name = "../env/GridWorld.x86_64" # Name of the Unity environment binary to launch
env = UnityEnv(env_name)
# Examine environment parameters
print(str(env))
# Reset the environment
initial_observation = env.reset()
for episode in range(10):
initial_observation = env.reset()
done = False
episode_rewards = 0
while not done:
observation, reward, done, info = env.step(env.action_space.sample())
episode_rewards += reward
print("Total reward this episode: {}".format(episode_rewards))
env.close()
# env=Reacher(render=True)
# env = UnityEnv(env_name, worker_id=10, use_visual=True, use_both=True)
# s, info = env.reset()
# for t in range(100):
# # env.render()
# s, r, done, info = env.step(GLOBAL_PPO.choose_action(s))
GLOBAL_PPO.save(model_path)
if args.test:
env = UnityEnv(env_name,
worker_id=np.random.randint(0, 10),
use_visual=True,
use_both=True)
env.reset()
GLOBAL_PPO = PPO()
GLOBAL_PPO.load(model_path)
test_steps = 200
test_episode = 10
for _ in range(test_episode):
s, info = env.reset()
''''''
# vector_s = info["brain_info"].vector_observations[0, :] # get the vector observation
# s=vector_s
for t in range(test_steps):
# env.render()
s, r, done, info = env.step(GLOBAL_PPO.choose_action(s))
''''''
# vector_s = info["brain_info"].vector_observations[0, :] # get the vector observation
Defining the environment related constants
'''
# Number of discrete states (bucket) per state dimension
MAZE_SIZE = (5, 5)
NUM_BUCKETS = MAZE_SIZE # one bucket per grid
# Number of discrete actions
NUM_ACTIONS = env.action_space.n # ["N", "S", "E", "W"]
STATE_BOUNDS = [(0.0, 4.0), (0.0, 4.0)]
MAX_T = np.prod(MAZE_SIZE, dtype=int) * 100
q_table = np.load('q_table.npy')
env.render()
# Reset the environment
obv = env.reset()
# the initial state
state_0 = state_to_bucket(obv)
total_reward = 0
for t in range(MAX_T):
# Select an action
action = select_action(state_0, 0)
# execute the action
obv, reward, done, _ = env.step(action)
# Observe the result
state = state_to_bucket(obv)
[p.join() for p in processes] # finished at the same time
td3_trainer.save_model(model_path)
print(rewards)
if args.test:
# choose env
# env_name="./tac_follow_new4"
# env_name="tac_follow_new4_random02"
env_name = "tac_follow_new4_random"
env = UnityEnv(env_name, worker_id=22, use_visual=False, use_both=True)
td3_trainer.load_model(model_path)
eps_r = []
for eps in range(20):
state, info = env.reset()
state0 = state
state = state_process(state, state0)
episode_reward = 0
for step in range(max_steps):
action = td3_trainer.policy_net.get_action(
state,
deterministic=DETERMINISTIC,
explore_noise_scale=0.0)
next_state, reward, done, info = env.step(action)
reward += 100
next_state = state_process(next_state, state0)
episode_reward += reward
state = next_state
if done:
import time
from gym_unity.envs import UnityEnv
env_name = "../envs/Cat_2/Cats.exe"
print("\nwith no render")
env = UnityEnv(env_name, no_graphics=False, multiagent=True, worker_id=1)
"""
res = []
for j in range(10):
"""
print(str(env))
ini_obs = env.reset()
curr_t = time.time()
for i in range(10000):
actions = [env.action_space.sample() for agent in range(env.number_agents)]
obs, rew, done, info = env.step(actions)
res = time.time() - curr_t
print("\nTime for 1000 step")
print(res)
print("\n\n")
"""
res = []
