class TD3MultiAgent:
def __init__(self):
self.max_action = 1
self.policy_freq = 2
self.policy_freq_it = 0
self.batch_size = 512
self.discount = 0.99
self.replay_buffer = int(1e5)
self.device = 'cuda'
self.state_dim = 24
self.action_dim = 2
self.max_action = 1
self.policy_noise = 0.1
self.agents = 1
self.random_period = 1e4
self.tau = 5e-3
self.replay_buffer = ReplayBuffer(self.replay_buffer)
self.actor = Actor(self.state_dim, self.action_dim, self.max_action).to(self.device)
self.actor_target = Actor(self.state_dim, self.action_dim, self.max_action).to(self.device)
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-4)
# self.actor.load_state_dict(torch.load('actor2.pth'))
# self.actor_target.load_state_dict(torch.load('actor2.pth'))
self.noise = OUNoise(2, 32)
self.critic = Critic(48, self.action_dim).to(self.device)
self.critic_target = Critic(48, self.action_dim).to(self.device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4)
def select_action_with_noise(self, state, i):
import pdb
ratio = len(self.replay_buffer)/self.random_period
if len(self.replay_buffer)>self.random_period:
state = torch.FloatTensor(state[i,:]).to(self.device)
action = self.actor(state).cpu().data.numpy()
if self.policy_noise != 0:
action = (action + self.noise.sample())
return action.clip(-self.max_action,self.max_action)
else:
q= self.noise.sample()
return q
def step(self, i):
if len(self.replay_buffer)>self.random_period/2:
# Sample mini batch
# if True:
import pdb
s, a, r, s_, d = self.replay_buffer.sample(self.batch_size)
state = torch.FloatTensor(s[:,i,:]).to(self.device)
action = torch.FloatTensor(a[:,i,:]).to(self.device)
next_state = torch.FloatTensor(s_[:,i,:]).to(self.device)
a_state = torch.FloatTensor(s).to(self.device).reshape(-1,48)
a_action = torch.FloatTensor(a).to(self.device).reshape(-1,4)
a_next_state = torch.FloatTensor(s_).to(self.device).reshape(-1,48)
done = torch.FloatTensor(1 - d[:,i]).to(self.device)
reward = torch.FloatTensor(r[:,i]).to(self.device)
# pdb.set_trace()
# Select action with the actor target and apply clipped noise
noise = torch.FloatTensor(a[:,i,:]).data.normal_(0, self.policy_noise).to(self.device)
noise = noise.clamp(-0.1,0.1) # NOISE CLIP WTF?
next_action = (self.actor_target(next_state) + noise).clamp(-self.max_action, self.max_action)
# Compute the target Q value
target_Q1, target_Q2 = self.critic_target(a_next_state, next_action)
target_Q = torch.min(target_Q1, target_Q2)
target_Q = reward.reshape(-1,1) + (done.reshape(-1,1) * self.discount * target_Q).detach()
# Get current Q estimates
current_Q1, current_Q2 = self.critic(a_state, action)
# Compute critic loss
critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
# Optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# Delayed policy updates
if self.policy_freq_it % self.policy_freq == 0:
# Compute actor loss
actor_loss = -self.critic.Q1(a_state, self.actor(state)).mean()
# Optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update the frozen target models
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
self.policy_freq_it += 1
return True
def reset(self):
self.policy_freq_it = 0
self.noise.reset()
class DyNODESacAgent(object):
"""DyNODE-SAC."""
def __init__(self,
obs_shape,
action_shape,
device,
model_kind,
kind='D',
step_MVE=5,
hidden_dim=256,
discount=0.99,
init_temperature=0.01,
alpha_lr=1e-3,
alpha_beta=0.9,
actor_lr=1e-3,
actor_beta=0.9,
actor_log_std_min=-10,
actor_log_std_max=2,
critic_lr=1e-3,
critic_beta=0.9,
critic_tau=0.005,
critic_target_update_freq=2,
model_lr=1e-3,
log_interval=100):
self.device = device
self.discount = discount
self.critic_tau = critic_tau
self.critic_target_update_freq = critic_target_update_freq
self.log_interval = log_interval
self.step_MVE = step_MVE
self.model_kind = model_kind
self.actor = Actor(obs_shape, action_shape, hidden_dim,
actor_log_std_min, actor_log_std_max).to(device)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),
lr=actor_lr,
betas=(actor_beta, 0.999))
self.critic = Critic(obs_shape, action_shape, hidden_dim).to(device)
self.critic_target = Critic(obs_shape, action_shape,
hidden_dim).to(device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
lr=critic_lr,
betas=(critic_beta, 0.999))
self.log_alpha = torch.tensor(np.log(init_temperature)).to(device)
self.log_alpha.requires_grad = True
self.target_entropy = -np.prod(
action_shape) # set target entropy to -|A|
self.log_alpha_optimizer = torch.optim.Adam([self.log_alpha],
lr=alpha_lr,
betas=(alpha_beta, 0.999))
if self.model_kind == 'dynode_model':
self.model = DyNODE(obs_shape,
action_shape,
hidden_dim_p=200,
hidden_dim_r=200).to(device)
elif self.model_kind == 'nn_model':
self.model = NN_Model(obs_shape,
action_shape,
hidden_dim_p=200,
hidden_dim_r=200,
kind=kind).to(device)
else:
assert 'model is not supported'
self.model_optimizer = torch.optim.Adam(self.model.parameters(),
lr=model_lr)
self.train()
self.critic_target.train()
def train(self, training=True):
self.training = training
self.actor.train(training)
self.critic.train(training)
self.model.train(training)
@property
def alpha(self):
return self.log_alpha.exp()
def select_action(self, obs):
with torch.no_grad():
obs = torch.FloatTensor(obs).to(self.device)
obs = obs.unsqueeze(0)
mu, _, _, _ = self.actor(obs,
compute_pi=False,
compute_log_pi=False)
return mu.cpu().data.numpy().flatten()
def sample_action(self, obs):
with torch.no_grad():
obs = torch.FloatTensor(obs).to(self.device)
obs = obs.unsqueeze(0)
mu, pi, _, _ = self.actor(obs, compute_log_pi=False)
return pi.cpu().data.numpy().flatten()
def update_model(self, replay_buffer, L, step):
if self.model_kind == 'dynode_model':
obs_m, action_m, reward_m, next_obs_m, _ = replay_buffer.sample_dynode(
)
transition_loss, reward_loss = self.model.loss(
obs_m, action_m, reward_m, next_obs_m)
model_loss = transition_loss + reward_loss
elif self.model_kind == 'nn_model':
obs, action, reward, next_obs, _ = replay_buffer.sample()
transition_loss, reward_loss = self.model.loss(
obs, action, reward, next_obs)
model_loss = transition_loss + reward_loss
else:
assert 'model is not supported'
# Optimize the Model
self.model_optimizer.zero_grad()
model_loss.backward()
self.model_optimizer.step()
if step % self.log_interval == 0:
L.log('train/model_loss', model_loss, step)
def MVE_prediction(self, replay_buffer, L, step):
obs, action, reward, next_obs, not_done = replay_buffer.sample()
trajectory = []
next_ob = next_obs
with torch.no_grad():
while len(trajectory) < self.step_MVE:
ob = next_ob
_, act, _, _ = self.actor(ob)
rew, next_ob = self.model(ob, act)
trajectory.append([ob, act, rew, next_ob])
_, next_action, log_pi, _ = self.actor(next_ob)
target_Q1, target_Q2 = self.critic_target(next_ob, next_action)
ret = torch.min(target_Q1,
target_Q2) - self.alpha.detach() * log_pi
critic_loss = 0
for ob, act, rew, _ in reversed(trajectory):
current_Q1, current_Q2 = self.critic(ob, act)
ret = rew + self.discount * ret
# critic_loss = critic_loss + utils.huber(current_Q1 - ret).mean() + utils.huber(current_Q2 - ret).mean()
critic_loss = critic_loss + F.mse_loss(
current_Q1, ret) + F.mse_loss(current_Q2, ret)
current_Q1, current_Q2 = self.critic(obs, action)
ret = reward + self.discount * ret
# critic_loss = critic_loss + utils.huber(current_Q1 - ret).mean() + utils.huber(current_Q2 - ret).mean()
critic_loss = critic_loss + F.mse_loss(current_Q1, ret) + F.mse_loss(
current_Q2, ret)
critic_loss = critic_loss / (self.step_MVE + 1)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# actor
_, pi, log_pi, log_std = self.actor(obs)
actor_Q1, actor_Q2 = self.critic(obs.detach(), pi)
actor_Q = torch.min(actor_Q1, actor_Q2)
actor_loss = (self.alpha.detach() * log_pi - actor_Q).mean()
# optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.log_alpha_optimizer.zero_grad()
alpha_loss = (self.alpha *
(-log_pi - self.target_entropy).detach()).mean()
alpha_loss.backward()
self.log_alpha_optimizer.step()
def update_critic(self, obs, action, reward, next_obs, not_done, L, step):
with torch.no_grad():
_, policy_action, log_pi, _ = self.actor(next_obs)
target_Q1, target_Q2 = self.critic_target(next_obs, policy_action)
target_V = torch.min(target_Q1,
target_Q2) - self.alpha.detach() * log_pi
target_Q = reward + (not_done * self.discount * target_V)
# get current Q estimates
current_Q1, current_Q2 = self.critic(obs, action)
critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(
current_Q2, target_Q)
if step % self.log_interval == 0:
L.log('train_critic/loss', critic_loss, step)
# Optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
self.critic.log(L, step)
def update_actor_and_alpha(self, obs, L, step):
_, pi, log_pi, log_std = self.actor(obs)
actor_Q1, actor_Q2 = self.critic(obs, pi)
actor_Q = torch.min(actor_Q1, actor_Q2)
actor_loss = (self.alpha.detach() * log_pi - actor_Q).mean()
if step % self.log_interval == 0:
L.log('train_actor/loss', actor_loss, step)
L.log('train_actor/target_entropy', self.target_entropy, step)
entropy = 0.5 * log_std.shape[1] * (
1.0 + np.log(2 * np.pi)) + log_std.sum(dim=-1)
if step % self.log_interval == 0:
L.log('train_actor/entropy', entropy.mean(), step)
# optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.actor.log(L, step)
self.log_alpha_optimizer.zero_grad()
alpha_loss = (self.alpha *
(-log_pi - self.target_entropy).detach()).mean()
if step % self.log_interval == 0:
L.log('train_alpha/loss', alpha_loss, step)
L.log('train_alpha/value', self.alpha, step)
alpha_loss.backward()
self.log_alpha_optimizer.step()
def update(self, replay_buffer, L, step):
if step < 2000:
for _ in range(2):
obs, action, reward, next_obs, not_done = replay_buffer.sample(
)
self.update_critic(obs, action, reward, next_obs, not_done, L,
step)
self.update_actor_and_alpha(obs, L, step)
if step % self.log_interval == 0:
L.log('train/batch_reward', reward.mean(), step)
else:
obs, action, reward, next_obs, not_done = replay_buffer.sample()
if step % self.log_interval == 0:
L.log('train/batch_reward', reward.mean(), step)
self.MVE_prediction(replay_buffer, L, step)
self.update_critic(obs, action, reward, next_obs, not_done, L,
step)
self.update_actor_and_alpha(obs, L, step)
if step % self.critic_target_update_freq == 0:
utils.soft_update_params(self.critic.Q1, self.critic_target.Q1,
self.critic_tau)
utils.soft_update_params(self.critic.Q2, self.critic_target.Q2,
self.critic_tau)
def save(self, model_dir, step):
torch.save(self.actor.state_dict(),
'%s/actor_%s.pt' % (model_dir, step))
torch.save(self.critic.state_dict(),
'%s/critic_%s.pt' % (model_dir, step))
def save_model(self, model_dir, step):
torch.save(self.model.state_dict(),
'%s/model_%s.pt' % (model_dir, step))
def load(self, model_dir, step):
self.actor.load_state_dict(
torch.load('%s/actor_%s.pt' % (model_dir, step)))
self.critic.load_state_dict(
torch.load('%s/critic_%s.pt' % (model_dir, step)))
class Agent():
""" Interacts with and learns from the environment. """
def __init__(self, state_size, action_size, fc1_units, fc2_units):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = torch.manual_seed(SEED)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, fc1_units,
fc2_units).to(device)
self.actor_target = Actor(state_size, action_size, fc1_units,
fc2_units).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size, fc1_units,
fc2_units).to(device)
self.critic_target = Critic(state_size, action_size, fc1_units,
fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OrnsteinUhlenbeck(action_size, SEED)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, SEED,
device)
def step(self, time_step, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(state, action, reward, next_state, done)
# Learn only every N_TIME_STEPS
if time_step % N_TIME_STEPS != 0:
return
# Learn if enough samples are available in replay buffer
if len(self.memory) > BATCH_SIZE:
for i in range(N_LEARN_UPDATES):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, add_noise=True):
""" Returns actions for given state as per current policy. """
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma):
"""Update policy and value parameters using given batch of experience tuples.
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.actor_target(next_states)
Q_targets_next = self.critic_target(next_states, actions_next)
# Compute Q targets from current states (y_i)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute critic loss
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def store(self):
torch.save(self.actor_local.state_dict(), 'checkpoint_actor.pth')
torch.save(self.critic_local.state_dict(), 'checkpoint_critic.pth')
def load(self):
if os.path.isfile('checkpoint_actor.pth') and os.path.isfile(
'checkpoint_critic.pth'):
print("=> loading checkpoints for Actor and Critic... ")
self.actor_local.load_state_dict('checkpoint_actor')
self.critic_local.load_state_dict('checkpoint_critic')
print("done !")
else:
print("no checkpoints found for Actor and Critic...")
class A2C():
def __init__(self, state_dim, action_dim, action_lim, update_type='soft',
lr_actor=1e-4, lr_critic=1e-3, tau=1e-3,
mem_size=1e6, batch_size=256, gamma=0.99,
other_cars=False, ego_dim=None):
self.device = torch.device("cuda:0" if torch.cuda.is_available()
else "cpu")
self.joint_model = False
if len(state_dim) == 3:
self.model = ActorCriticCNN(state_dim, action_dim, action_lim)
self.model_optim = optim.Adam(self.model.parameters(), lr=lr_actor)
self.target_model = ActorCriticCNN(state_dim, action_dim, action_lim)
self.target_model.load_state_dict(self.model.state_dict())
self.model.to(self.device)
self.target_model.to(self.device)
self.joint_model = True
else:
self.actor = Actor(state_dim, action_dim, action_lim, other_cars=other_cars, ego_dim=ego_dim)
self.actor_optim = optim.Adam(self.actor.parameters(), lr=lr_actor)
self.target_actor = Actor(state_dim, action_dim, action_lim, other_cars=other_cars, ego_dim=ego_dim)
self.target_actor.load_state_dict(self.actor.state_dict())
self.target_actor.eval()
self.critic = Critic(state_dim, action_dim, other_cars=other_cars, ego_dim=ego_dim)
self.critic_optim = optim.Adam(self.critic.parameters(), lr=lr_critic, weight_decay=1e-2)
self.target_critic = Critic(state_dim, action_dim, other_cars=other_cars, ego_dim=ego_dim)
self.target_critic.load_state_dict(self.critic.state_dict())
self.target_critic.eval()
self.actor.to(self.device)
self.target_actor.to(self.device)
self.critic.to(self.device)
self.target_critic.to(self.device)
self.action_lim = action_lim
self.tau = tau # hard update if tau is None
self.update_type = update_type
self.batch_size = batch_size
self.gamma = gamma
if self.joint_model:
mem_size = mem_size//100
self.memory = Memory(int(mem_size), action_dim, state_dim)
mu = np.zeros(action_dim)
sigma = np.array([0.5, 0.05])
self.noise = OrnsteinUhlenbeckActionNoise(mu, sigma)
self.target_noise = OrnsteinUhlenbeckActionNoise(mu, sigma)
self.initialised = True
self.training = False
def select_action(self, obs):
with torch.no_grad():
obs = torch.FloatTensor(np.expand_dims(obs, axis=0)).to(self.device)
if self.joint_model:
action, _ = self.model(obs)
action = action.data.cpu().numpy().flatten()
else:
action = self.actor(obs).data.cpu().numpy().flatten()
if self.training:
action += self.noise()
return action
else:
return action
def append(self, obs0, action, reward, obs1, terminal1):
self.memory.append(obs0, action, reward, obs1, terminal1)
def reset_noise(self):
self.noise.reset()
self.target_noise.reset()
def train(self):
if self.joint_model:
self.model.train()
self.target_model.train()
else:
self.actor.train()
self.target_actor.train()
self.critic.train()
self.target_critic.train()
self.training = True
def eval(self):
if self.joint_model:
self.model.eval()
self.target_model.eval()
else:
self.actor.eval()
self.target_actor.eval()
self.critic.eval()
self.target_critic.eval()
self.training = False
def save(self, folder, episode, previous=None, solved=False):
filename = lambda type, ep : folder + '%s' % type + \
(not solved) * ('_ep%d' % (ep)) + \
(solved * '_solved') + '.pth'
if self.joint_model:
torch.save(self.model.state_dict(), filename('model', episode))
torch.save(self.target_model.state_dict(), filename('target_model', episode))
else:
torch.save(self.actor.state_dict(), filename('actor', episode))
torch.save(self.target_actor.state_dict(), filename('target_actor', episode))
torch.save(self.critic.state_dict(), filename('critic', episode))
torch.save(self.target_critic.state_dict(), filename('target_critic', episode))
if previous is not None and previous > 0:
if self.joint_model:
os.remove(filename('model', previous))
os.remove(filename('target_model', previous))
else:
os.remove(filename('actor', previous))
os.remove(filename('target_actor', previous))
os.remove(filename('critic', previous))
os.remove(filename('target_critic', previous))
def load_actor(self, actor_filepath):
qualifier = '_' + actor_filepath.split("_")[-1]
folder = actor_filepath[:actor_filepath.rfind("/")+1]
filename = lambda type : folder + '%s' % type + qualifier
if self.joint_model:
self.model.load_state_dict(torch.load(filename('model'),
map_location=self.device))
self.target_model.load_state_dict(torch.load(filename('target_model'),
map_location=self.device))
else:
self.actor.load_state_dict(torch.load(filename('actor'),
map_location=self.device))
self.target_actor.load_state_dict(torch.load(filename('target_actor'),
map_location=self.device))
def load_all(self, actor_filepath):
self.load_actor(actor_filepath)
qualifier = '_' + actor_filepath.split("_")[-1]
folder = actor_filepath[:actor_filepath.rfind("/")+1]
filename = lambda type : folder + '%s' % type + qualifier
if not self.joint_model:
self.critic.load_state_dict(torch.load(filename('critic'),
map_location=self.device))
self.target_critic.load_state_dict(torch.load(filename('target_critic'),
map_location=self.device))
def update(self, target_noise=True):
try:
minibatch = self.memory.sample(self.batch_size) # dict of ndarrays
except ValueError as e:
print('Replay memory not big enough. Continue.')
return None, None
states = Variable(torch.FloatTensor(minibatch['obs0'])).to(self.device)
actions = Variable(torch.FloatTensor(minibatch['actions'])).to(self.device)
rewards = Variable(torch.FloatTensor(minibatch['rewards'])).to(self.device)
next_states = Variable(torch.FloatTensor(minibatch['obs1'])).to(self.device)
terminals = Variable(torch.FloatTensor(minibatch['terminals1'])).to(self.device)
if self.joint_model:
target_actions, _ = self.target_model(next_states)
if target_noise:
for sample in range(target_actions.shape[0]):
target_actions[sample] += self.target_noise()
target_actions[sample].clamp(-self.action_lim, self.action_lim)
_, target_qvals = self.target_model(next_states, target_actions=target_actions)
y = rewards + self.gamma * (1 - terminals) * target_qvals
_, model_qvals = self.model(states, target_actions=actions)
value_loss = F.mse_loss(y, model_qvals)
model_actions, _ = self.model(states)
_, model_qvals = self.model(states, target_actions=model_actions)
action_loss = -model_qvals.mean()
self.model_optim.zero_grad()
(value_loss + action_loss).backward()
self.model_optim.step()
else:
target_actions = self.target_actor(next_states)
if target_noise:
for sample in range(target_actions.shape[0]):
target_actions[sample] += self.target_noise()
target_actions[sample].clamp(-self.action_lim, self.action_lim)
target_critic_qvals = self.target_critic(next_states, target_actions)
y = rewards + self.gamma * (1 - terminals) * target_critic_qvals
# optimise critic
critic_qvals = self.critic(states, actions)
value_loss = F.mse_loss(y, critic_qvals)
self.critic_optim.zero_grad()
value_loss.backward()
self.critic_optim.step()
# optimise actor
action_loss = -self.critic(states, self.actor(states)).mean()
self.actor_optim.zero_grad()
action_loss.backward()
self.actor_optim.step()
# optimise target networks
if self.update_type == 'soft':
if self.joint_model:
soft_update(self.target_model, self.model, self.tau)
else:
soft_update(self.target_actor, self.actor, self.tau)
soft_update(self.target_critic, self.critic, self.tau)
else:
if self.joint_model:
hard_update(self.target_model, self.model)
else:
hard_update(self.target_actor, self.actor)
hard_update(self.target_critic, self.critic)
return action_loss.item(), value_loss.item()
avg_loss_critic = torch.tensor(loss_critic).mean().item()
win_score = vis.line(
X=[i], Y=[avg_score], win=win_score,
update="append") if win_score else vis.line(
X=[i],
Y=[avg_score],
opts=dict(title="Average Score", xlabel="Episodes"))
win_actor_score = vis.line(
X=[i], Y=[avg_score_actor], win=win_actor_score,
update="append") if win_actor_score else vis.line(
X=[i],
Y=[avg_score_actor],
opts=dict(title="Average Actor Score", xlabel="Episodes"))
win_critic_loss = vis.line(
X=[i], Y=[avg_loss_critic], win=win_critic_loss,
update="append") if win_critic_loss else vis.line(
X=[i],
Y=[avg_loss_critic],
opts=dict(title="Average Critic Loss", xlabel="Episodes"))
print(
"Episode: {}/{}, Score: {:.4f}, Score Actor: {:.4f}, Loss Critic: {:.7f}, Noise Std: {:.4f}"
.format(i + 1, args.episodes, avg_score, avg_score_actor,
avg_loss_critic, noise_std))
score_actor = []
loss_critic = []
score = 0
env.close()
torch.save(actor.state_dict(), "checkpoint_ddpg.pth")
class DDPG():
""" Deep Deterministic Policy Gradients Agent used to interaction with and learn from an environment """
def __init__(self, state_size: int, action_size: int, num_agents: int,
epsilon, random_seed: int):
""" Initialize a DDPG Agent Object
:param state_size: dimension of state (input)
:param action_size: dimension of action (output)
:param num_agents: number of concurrent agents in the environment
:param epsilon: initial value of epsilon for exploration
:param random_seed: random seed
"""
self.state_size = state_size
self.action_size = action_size
self.num_agents = num_agents
self.seed = random.seed(random_seed)
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.t_step = 0
# Hyperparameters
self.buffer_size = 1000000
self.batch_size = 128
self.update_every = 10
self.num_updates = 10
self.gamma = 0.99
self.tau = 0.001
self.lr_actor = 0.0001
self.lr_critic = 0.001
self.weight_decay = 0
self.epsilon = epsilon
self.epsilon_decay = 0.97
self.epsilon_min = 0.005
# Networks (Actor: State -> Action, Critic: (State,Action) -> Value)
self.actor_local = Actor(self.state_size, self.action_size,
random_seed).to(self.device)
self.actor_target = Actor(self.state_size, self.action_size,
random_seed).to(self.device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=self.lr_actor)
self.critic_local = Critic(self.state_size, self.action_size,
random_seed).to(self.device)
self.critic_target = Critic(self.state_size, self.action_size,
random_seed).to(self.device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=self.lr_critic,
weight_decay=self.weight_decay)
# Initialize actor and critic networks to start with same parameters
self.soft_update(self.actor_local, self.actor_target, tau=1)
self.soft_update(self.critic_local, self.critic_target, tau=1)
# Noise Setup
self.noise = OUNoise(self.action_size, random_seed)
# Replay Buffer Setup
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
def __str__(self):
return "DDPG_Agent"
def train(self,
env,
brain_name,
num_episodes=200,
max_time=1000,
print_every=10):
""" Interacts with and learns from a given Unity Environment
:param env: Unity Environment the agents is trying to learn
:param brain_name: Brain for Environment
:param num_episodes: Number of episodes to train
:param max_time: How long each episode runs for
:param print_every: How often in episodes to print a running average
:return: Returns episodes scores and 100 episode averages as lists
"""
# --------- Set Everything up --------#
scores = []
avg_scores = []
scores_deque = deque(maxlen=print_every)
# -------- Simulation Loop --------#
for episode_num in range(1, num_episodes + 1):
# Reset everything
env_info = env.reset(train_mode=True)[brain_name]
states = env_info.vector_observations
episode_scores = np.zeros(self.num_agents)
self.reset_noise()
# Run the episode
for t in range(max_time):
actions = self.act(states, self.epsilon)
env_info = env.step(actions)[brain_name]
next_states, rewards, dones = env_info.vector_observations, env_info.rewards, env_info.local_done
self.step(states, actions, rewards, next_states, dones)
episode_scores += rewards
states = next_states
if np.any(dones):
break
# -------- Episode Finished ---------#
self.epsilon *= self.epsilon_decay
self.epsilon = max(self.epsilon, self.epsilon_min)
scores.append(np.mean(episode_scores))
scores_deque.append(np.mean(episode_scores))
avg_scores.append(np.mean(scores_deque))
if episode_num % print_every == 0:
print(
f'Episode: {episode_num} \tAverage Score: {round(np.mean(scores_deque), 2)}'
)
torch.save(
self.actor_local.state_dict(),
f'{PATH}\checkpoints\{self.__str__()}_Actor_Multiple.pth')
torch.save(
self.critic_local.state_dict(),
f'{PATH}\checkpoints\{self.__str__()}_Critic_Multiple.pth')
# -------- All Episodes finished Save parameters and scores --------#
# Save Model Parameters
torch.save(self.actor_local.state_dict(),
f'{PATH}\checkpoints\{self.__str__()}_Actor_Multiple.pth')
torch.save(self.critic_local.state_dict(),
f'{PATH}\checkpoints\{self.__str__()}_Critic_Multiple.pth')
# Save mean score per episode (of the 20 agents)
f = open(f'{PATH}\scores\{self.__str__()}_Multiple_Scores.txt', 'w')
scores_string = "\n".join([str(score) for score in scores])
f.write(scores_string)
f.close()
# Save average scores for 100 window average
f = open(f'{PATH}\scores\{self.__str__()}_Multiple_AvgScores.txt', 'w')
avgScores_string = "\n".join([str(score) for score in avg_scores])
f.write(avgScores_string)
f.close()
return scores, avg_scores
def step(self, states, actions, rewards, next_states, dones):
""" what the agent needs to do for every time step that occurs in the environment. Takes
in a (s,a,r,s',d) tuple and saves it to memeory and learns from experiences. Note: this is not
the same as a step in the environment. Step is only called once per environment time step.
:param states: array of states agent used to select actions
:param actions: array of actions taken by agents
:param rewards: array of rewards for last action taken in environment
:param next_states: array of next states after actions were taken
:param dones: array of bools representing if environment is finished or not
"""
# Save experienced in replay memory
for agent_num in range(self.num_agents):
self.memory.add(states[agent_num], actions[agent_num],
rewards[agent_num], next_states[agent_num],
dones[agent_num])
# Learn "num_updates" times every "update_every" time step
self.t_step += 1
if len(self.memory
) > self.batch_size and self.t_step % self.update_every == 0:
self.t_step = 0
for _ in range(self.num_updates):
experiences = self.memory.sample()
self.learn(experiences)
def act(self, states, epsilon, add_noise=True):
""" Returns actions for given states as per current policy. Policy comes from the actor network.
:param states: array of states from the environment
:param epsilon: probability of exploration
:param add_noise: bool on whether or not to potentially have exploration for action
:return: clipped actions
"""
states = torch.from_numpy(states).float().to(self.device)
self.actor_local.eval() # Sets to eval mode (no gradients)
with torch.no_grad():
actions = self.actor_local(states).cpu().data.numpy()
self.actor_local.train() # Sets to train mode (gradients back on)
if add_noise and epsilon > np.random.random():
actions += [self.noise.sample() for _ in range(self.num_agents)]
return np.clip(actions, -1, 1)
def reset_noise(self):
""" resets to noise parameters """
self.noise.reset()
def learn(self, experiences):
""" Update actor and critic networks using a given batch of experiences
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(states) -> actions
critic_target(states, actions) -> Q-value
:param experiences: tuple of arrays (states, actions, rewards, next_states, dones) sampled from the replay buffer
"""
states, actions, rewards, next_states, dones = experiences
# -------------------- Update Critic -------------------- #
# Use target networks for getting next actions and q values and calculate q_targets
next_actions = self.actor_target(next_states)
next_q_targets = self.critic_target(next_states, next_actions)
q_targets = rewards + (self.gamma * next_q_targets * (1 - dones))
# Compute critic loss (Same as DQN Loss)
q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(q_expected, q_targets)
# Minimize loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# -------------------- Update Actor --------------------- #
# Computer actor loss (maximize mean of Q(states,actions))
action_preds = self.actor_local(states)
# Optimizer minimizes and we want to maximize so multiply by -1
actor_loss = -1 * self.critic_local(states, action_preds).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
#---------------- Update Target Networks ---------------- #
self.soft_update(self.critic_local, self.critic_target, self.tau)
self.soft_update(self.actor_local, self.actor_target, self.tau)
def soft_update(self, local_network, target_network, tau):
""" soft update newtwork parametes
θ_target = τ*θ_local + (1 - τ)*θ_target
:param local_network: PyTorch Network that is always up to date
:param target_network: PyTorch Network that is not up to date
:param tau: update (interpolation) parameter
"""
for target_param, local_param in zip(target_network.parameters(),
local_network.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)