def __init__(self, env, agent_params):
super().__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.n_drivers = self.agent_params['n_drivers']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size_ac'],
self.agent_params['shared_exp'],
self.agent_params['shared_exp_lambda'],
self.agent_params['is_city'],
self.agent_params['learning_rate'],
self.agent_params['n_drivers']
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.num_critic_updates_per_agent_update = agent_params[
'num_critic_updates_per_agent_update']
self.num_actor_updates_per_agent_update = agent_params[
'num_actor_updates_per_agent_update']
self.device = agent_params['device']
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['device'],
discrete=self.agent_params['discrete'],
learning_rate=self.agent_params['learning_rate'],
)
# introduced in actor-critic to improve advantage function.
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params, **kwargs):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
# actor/policy
if self.agent_params['discrete']:
self.actor = DiscreteMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
else:
self.actor = ContinuousMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
self.policy_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.critic_loss = tf.keras.losses.MeanSquaredError()
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic.nn_critic.compile(optimizer=self.critic_optimizer, loss=self.critic_loss)
self.replay_buffer = ReplayBuffer()
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode: DONE
for _ in range(self.agent_params['num_critic_updates_per_agent_update']):
critic_loss = self.critic.update(ob_no, ac_na, next_ob_no, re_n, terminal_n)
advantage = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n)
for _ in range(self.agent_params['num_actor_updates_per_agent_update']):
actor_loss = self.actor.update(ob_no, ac_na, advantage)
loss = OrderedDict()
loss['Critic_Loss'] = critic_loss
loss['Actor_Loss'] = actor_loss
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode: DONE
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
V_s = self.critic.forward_np(ob_no)
V_s_prime = self.critic.forward_np(next_ob_no)
Q_s_a = re_n + self.gamma*V_s_prime*(1-terminal_n)
adv_n = Q_s_a - V_s
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent:
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.num_critic_updates_per_agent_update = agent_params['num_critic_updates_per_agent_update']
self.num_actor_updates_per_agent_update = agent_params['num_actor_updates_per_agent_update']
self.device = agent_params['device']
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.actor = MLPPolicyAC(self.agent_params['ob_dim'],
self.agent_params['ac_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['device'],
discrete=self.agent_params['discrete'],
learning_rate=self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer(agent_params['replay_size'])
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
ob, next_ob, rew, done = map(lambda x: torch.from_numpy(x).to(self.device), [ob_no, next_ob_no, re_n, terminal_n])
value = self.critic.value_func(ob).squeeze()
next_value = self.critic.value_func(next_ob).squeeze() * (1 - done)
adv_n = rew + (self.gamma * next_value) - value
adv_n = adv_n.cpu().detach().numpy()
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
loss = OrderedDict()
for critic_update in range(self.num_critic_updates_per_agent_update):
loss['Critic_Loss'] = self.critic.update(ob_no, next_ob_no, re_n, terminal_n)
adv_n = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n) # put final critic loss here
for actor_update in range(self.num_actor_updates_per_agent_update):
loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, adv_n) # put final actor loss here
return loss
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params["gamma"]
self.standardize_advantages = self.agent_params["standardize_advantages"]
self.actor = MLPPolicyAC(
self.agent_params["ac_dim"],
self.agent_params["ob_dim"],
self.agent_params["n_layers"],
self.agent_params["size"],
self.agent_params["discrete"],
self.agent_params["learning_rate"],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.gae = self.agent_params['gae']
self.gae_lambda = self.agent_params['gae_lambda']
self.ppo = self.agent_params['ppo']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
if self.ppo:
self.old_actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
self.old_actor.load_state_dict(self.actor.state_dict())
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.gae = self.agent_params['gae']
self.gae_lambda = self.agent_params['gae_lambda']
self.ppo = self.agent_params['ppo']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
if self.ppo:
self.old_actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
self.old_actor.load_state_dict(self.actor.state_dict())
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
rewards = np.concatenate([r for r in re_n]) if self.gae else re_n
assert rewards.shape == terminal_n.shape
for i in range(
self.agent_params['num_critic_updates_per_agent_update']):
loss_critic = self.critic.update(ob_no, ac_na, next_ob_no, rewards,
terminal_n)
advantage = self.estimate_advantage(ob_no, next_ob_no, re_n,
terminal_n)
old_log_prob = self.get_old_prob(self.old_actor, ob_no,
ac_na) if self.ppo else None
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for i in range(
self.agent_params['num_actor_updates_per_agent_update']):
loss_actor = self.actor.update(ob_no, ac_na, advantage,
old_log_prob)
if self.ppo:
self.old_actor.load_state_dict(self.actor.state_dict())
loss = OrderedDict()
loss['Critic_Loss'] = loss_critic
loss['Actor_Loss'] = loss_actor
return loss
def get_old_prob(self, old_policy, ob_no, ac_na):
observations = ptu.from_numpy(ob_no)
actions = ptu.from_numpy(ac_na)
log_prob = old_policy.forward(observations).log_prob(actions)
return ptu.to_numpy(log_prob)
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
v_s = self.critic.forward_np(ob_no)
if not self.gae:
v_s_next = self.critic.forward_np(next_ob_no) * (1 - terminal_n)
adv_n = re_n + self.gamma * v_s_next - v_s
else:
index = 0
adv_n = np.zeros(len(ob_no))
for rewards in re_n:
gae_deltas = []
for i in range(len(rewards) - 1):
delta = rewards[i] + self.gamma * v_s[index + i +
1] - v_s[index + i]
gae_deltas.append(delta)
i = len(rewards) - 1
gae_deltas.append(rewards[i] - v_s[index + i])
assert len(gae_deltas) == len(rewards)
sum_deltas = 0
for t in range(len(gae_deltas) - 1, -1, -1):
sum_deltas = gae_deltas[
t] + sum_deltas * self.gamma * self.gae_lambda
adv_n[t + index] = sum_deltas
index += len(rewards)
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
concat_rew = False if self.gae else True
return self.replay_buffer.sample_recent_data(batch_size, concat_rew)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
ob_no = ptu.from_numpy(ob_no)
next_ob_no = ptu.from_numpy(next_ob_no)
terminal_n = ptu.from_numpy(terminal_n)
re_n = ptu.from_numpy(re_n)
ac_na = ptu.from_numpy(ac_na)
loss_critic = 0.
for i in range(
self.agent_params['num_critic_updates_per_agent_update']):
loss_critic += self.critic.update(ob_no, ac_na, next_ob_no, re_n,
terminal_n)
# advantage = estimate_advantage(...) :
adv_n = self.estimate_advantage(ob_no, next_ob_no, re_n,
terminal_n) # a tensor is returned
loss_actor = 0.
for i in range(
self.agent_params['num_actor_updates_per_agent_update']):
loss_actor += self.actor.update(ob_no, ac_na, adv_n)
loss = OrderedDict()
loss['Critic_Loss'] = loss_critic
loss[
'Actor_Loss'] = loss_actor # in TensorBoard, loss_actor actually increases as we actually minimize -loss_actor
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
# V_s_prime = self.critic.critic_network(next_ob_no)
# V_s_prime = V_s_prime.squeeze()
# mask = (terminal_n == 1.)
# V_s_prime= V_s_prime.masked_fill(mask, 0.)
#
# V_s = self.critic.critic_network(ob_no)
# V_s = V_s.squeeze()
# # assert V_s_prime.ndim == V_s.ndim # TODO-assert enable this assert in debug
# adv_n2 = re_n + self.gamma * V_s_prime - V_s
# another way to calculate:
V_s_prime = re_n + (
1 - terminal_n) * self.gamma * self.critic.forward(next_ob_no)
adv_n = V_s_prime - self.critic.forward(ob_no)
# assert adv_n2 == adv_n
if self.standardize_advantages:
adv_n = (adv_n - torch.mean(adv_n)) / (torch.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent:
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.num_critic_updates_per_agent_update = agent_params[
'num_critic_updates_per_agent_update']
self.num_actor_updates_per_agent_update = agent_params[
'num_actor_updates_per_agent_update']
self.device = agent_params['device']
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['device'],
discrete=self.agent_params['discrete'],
learning_rate=self.agent_params['learning_rate'],
)
# introduced in actor-critic to improve advantage function.
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
ob, next_ob, rew, done = map(
lambda x: torch.from_numpy(x).to(self.device),
[ob_no, next_ob_no, re_n, terminal_n])
# DoneTODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
v_s = self.critic.value_func(ob)
v_s_prime = self.critic.value_func(next_ob).squeeze()
v_s_prime[done >= 1] = 0
estimated_q = rew + self.gamma * v_s_prime
adv_n = estimated_q - v_s
adv_n = adv_n.cpu().detach().numpy()
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# DoneTODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
# advantage = estimate_advantage(...)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
loss = OrderedDict()
for i in range(self.num_critic_updates_per_agent_update):
loss['Critic_Loss'] = self.critic.update(ob_no, next_ob_no, re_n,
terminal_n)
adv = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n)
for i in range(self.num_actor_updates_per_agent_update):
loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, adv)
return loss
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
# ob_no = ptu.from_numpy(ob_no)
# ac_na = ptu.from_numpy(ac_na).to(torch.long)
# next_ob_no = ptu.from_numpy(next_ob_no)
# re_n = ptu.from_numpy(re_n)
# terminal_n = ptu.from_numpy(terminal_n)
for _ in range(
self.agent_params['num_critic_updates_per_agent_update']):
critic_loss = self.critic.update(ob_no=ob_no,
ac_na=ac_na,
reward_n=re_n,
next_ob_no=next_ob_no,
terminal_n=terminal_n)
# targets = re_n + self.gamma * self.critic(next_ob_no) * (1-terminal_n)
# pred = self.critic(ob_no)
# #advantage = re_n + self.gamma * self.critic(next_ob_no) * (1-terminal_n) - self.critic(ob_no)
# advantage = targets - pred
advantage = self.estimate_advantage(ob_no, next_ob_no, re_n,
terminal_n)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for _ in range(
self.agent_params['num_actor_updates_per_agent_update']):
actor_loss = self.actor.update(ob_no, ac_na, adv_n=advantage)
loss = OrderedDict()
loss['Critic_Loss'] = critic_loss
loss['Actor_Loss'] = actor_loss
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
value_targets = re_n + self.gamma * self.critic(next_ob_no) * (
1. - terminal_n)
value_pred = self.critic(ob_no)
#advantage = re_n + self.gamma * self.critic(next_ob_no) * (1-terminal_n) - self.critic(ob_no)
adv_n = value_targets - value_pred
if self.standardize_advantages:
adv_n = (adv_n - torch.mean(adv_n)) / (torch.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, sess, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.sess = sess
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.actor = MLPPolicyAC(sess,
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
discrete=self.agent_params['discrete'],
learning_rate=self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(sess, self.agent_params)
self.replay_buffer = ReplayBuffer()
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
vs = self.sess.run(self.critic.critic_prediction, feed_dict = {self.critic.sy_ob_no : ob_no})
vsprime = self.sess.run(self.critic.critic_prediction, feed_dict = {self.critic.sy_ob_no : next_ob_no})*(1-terminal_n)
q_val = re_n + self.gamma * vsprime
adv_n = q_val - vs
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
# advantage = estimate_advantage(...)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for x in range(self.agent_params['num_critic_updates_per_agent_update']):
closs = self.critic.update(ob_no, next_ob_no, re_n, terminal_n)
advantage = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n)
for x in range(self.agent_params['num_actor_updates_per_agent_update']):
aloss = self.actor.update(ob_no, ac_na, advantage)
loss = OrderedDict()
loss['Critic_Loss'] = closs # put final critic loss here
loss['Actor_Loss'] = aloss # put final actor loss here
return loss
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super().__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.n_drivers = self.agent_params['n_drivers']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size_ac'],
self.agent_params['shared_exp'],
self.agent_params['shared_exp_lambda'],
self.agent_params['is_city'],
self.agent_params['learning_rate'],
self.agent_params['n_drivers']
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
loss = OrderedDict()
for i in range(self.agent_params['num_critic_updates_per_agent_update']):
if not self.agent_params['shared_exp']:
loss['Critic_Loss'] = self.critic.update(ob_no, ac_na, next_ob_no, re_n, terminal_n)
else:
action_distributions = self.actor.shared_forward(ptu.from_numpy(ob_no))
loss['Critic_Loss'] = self.critic.update(ob_no, ac_na, next_ob_no, re_n, terminal_n, action_distributions)
# advantage = estimate_advantage(...)
if self.agent_params['shared_exp']:
advantage = self.estimate_shared_advantage(ob_no, next_ob_no, re_n, terminal_n)
else:
advantage = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for i in range(self.agent_params['num_actor_updates_per_agent_update']):
loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, advantage)
return loss
def estimate_shared_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
value_s = self.critic.shared_forward(ptu.from_numpy(ob_no))
value_next_s = self.critic.shared_forward(ptu.from_numpy(next_ob_no))
adv_n = dict()
for i in range(self.n_drivers):
for k in range(self.n_drivers):
adv_n[(i,k)] = re_n[:,k] + self.gamma*ptu.to_numpy(value_next_s[(i,k)]) - ptu.to_numpy(value_s[(i,k)])
if self.standardize_advantages:
adv_n[(i,k)] = (adv_n[(i,k)]- np.mean(adv_n[(i,k)]))/(np.std(adv_n[(i,k)])+1e-8)
return adv_n
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
value_s = self.critic.forward_np(ob_no)
value_next_s = self.critic.forward_np(next_ob_no)
value_next_s[terminal_n==1] = 0
adv_n = re_n + self.gamma*value_next_s - value_s
if self.standardize_advantages:
for i in range(self.n_drivers):
adv_n[:,i] = (adv_n[:,i] - np.mean(adv_n[:,i])) / (np.std(adv_n[:,i]) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
loss = OrderedDict()
for _ in range(
self.agent_params['num_critic_updates_per_agent_update']):
loss['Critic_Loss'] = self.critic.update(ob_no, ac_na, next_ob_no,
re_n, terminal_n)
advantages = self.estimate_advantage(ob_no, next_ob_no, re_n,
terminal_n)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for _ in range(
self.agent_params['num_actor_updates_per_agent_update']):
loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, advantages)
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
ob_no = ptu.from_numpy(ob_no)
next_ob_no = ptu.from_numpy(next_ob_no)
re_n = ptu.from_numpy(re_n)
terminal_n = ptu.from_numpy(terminal_n).bool()
v_s = self.critic(ob_no)
v_sp1 = self.critic(next_ob_no)
v_sp1[terminal_n] = 0
q_sa = re_n + self.gamma * v_sp1
adv_n = q_sa - v_s
assert adv_n.size() == re_n.size()
adv_n = adv_n.detach().cpu().numpy()
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params, **kwargs):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
# actor/policy
if self.agent_params['discrete']:
self.actor = DiscreteMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
else:
self.actor = ContinuousMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
self.policy_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.critic_loss = tf.keras.losses.MeanSquaredError()
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic.nn_critic.compile(optimizer=self.critic_optimizer, loss=self.critic_loss)
self.replay_buffer = ReplayBuffer()
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
current_state_values = self.critic(ob_no)
next_state_values = self.gamma * self.critic(next_ob_no) * (1.0 - tf.expand_dims(tf.cast(terminal_n, tf.float32), axis=1))
adv_n = next_state_values + re_n - current_state_values
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
# advantage = estimate_advantage(...)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
for _ in range(self.agent_params['num_critic_updates_per_agent_update']):
for _ in range(self.agent_params['num_target_updates']):
critic_targets = self.critic.get_training_targets(next_ob_no, re_n, terminal_n, self.gamma)
critic_dataset = tf.data.Dataset.from_tensor_slices(
(tf.cast(ob_no, tf.float32), tf.cast(critic_targets, tf.float32)))
critic_dataset = critic_dataset.batch(batch_size=critic_targets.shape[0]).repeat()
self.critic.nn_critic.fit(critic_dataset, epochs=1,
steps_per_epoch=self.agent_params['num_grad_steps_per_target_update'])
advantage = tf.stop_gradient(self.estimate_advantage(ob_no, next_ob_no, tf.expand_dims(re_n, axis=1), terminal_n))
for _ in range(self.agent_params['num_actor_updates_per_agent_update']):
with tf.GradientTape() as tape:
log_action_probas = self.actor.get_log_prob(ob_no, ac_na)
loss = -tf.reduce_mean(advantage * tf.expand_dims(log_action_probas, axis=1))
actor_vars = self.actor.trainable_variables
grads = tape.gradient(loss, actor_vars)
self.policy_optimizer.apply_gradients(zip(grads, actor_vars))
loss_dict = OrderedDict()
loss_dict['Critic_Loss'] = 0 # put final critic loss here
loss_dict['Actor_Loss'] = loss.numpy().item() # put final actor loss here
return loss_dict
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
loss = OrderedDict()
ob_no = ptu.from_numpy(ob_no)
ac_na = ptu.from_numpy(ac_na)
re_n = ptu.from_numpy(re_n)
next_ob_no = ptu.from_numpy(next_ob_no)
terminal_n = ptu.from_numpy(terminal_n)
for _ in range(
self.agent_params['num_critic_updates_per_agent_update']):
loss['Critic_Loss'] = self.critic.update(ob_no, ac_na, next_ob_no,
re_n, terminal_n)
advantages = self.estimate_advantage(ob_no, next_ob_no, re_n,
terminal_n)
advantages = ptu.from_numpy(advantages)
for _ in range(
self.agent_params['num_actor_updates_per_agent_update']):
loss['Actor_Loss'] = self.actor.update(ob_no,
ac_na,
adv_n=advantages)
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
v_s_n = self.critic(ob_no)
v_s_prime_n = self.critic(next_ob_no)
# setting V(s') to zero if the next state is a terminal state
q_n = re_n + self.gamma * v_s_prime_n * (1 - terminal_n)
adv_n = q_n - v_s_n
assert adv_n.size() == re_n.size()
adv_n = adv_n.detach().cpu().numpy()
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)
class ACAgent(BaseAgent):
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# TODO_ Implement the following pseudocode:
# for agent_params['num_critic_updates_per_agent_update'] steps,
# update the critic
# advantage = estimate_advantage(...)
# for agent_params['num_actor_updates_per_agent_update'] steps,
# update the actor
loss = OrderedDict()
Critic_Loss = []
Actor_loss = []
for i in range(agent_params['num_critic_updates_per_agent_update']):
Critic_Loss.append(
self.critic.update(ob_no, ac_na, next_ob_no, re_n, terminal_n))
advantage = estimate_advantage(ob_no, next_ob_no, re_n, terminal_n)
for i in range(agent_params['num_actor_updates_per_agent_update']):
Actor_loss.append(self.actor.update(ob_no, ac_na, advantage))
loss['Critic_Loss'] = mean(Critic_Loss)
loss['Actor_Loss'] = mean(Actor_loss)
return loss
def estimate_advantage(self, ob_no, next_ob_no, re_n, terminal_n):
# TODO_ Implement the following pseudocode:
# 1) query the critic with ob_no, to get V(s)
# 2) query the critic with next_ob_no, to get V(s')
# 3) estimate the Q value as Q(s, a) = r(s, a) + gamma*V(s')
# HINT: Remember to cut off the V(s') term (ie set it to 0) at terminal states (ie terminal_n=1)
# 4) calculate advantage (adv_n) as A(s, a) = Q(s, a) - V(s)
Vs = self.critic(ob_no)
Vs_prime = self.critic(next_ob_no)
# ternimal_index = next(i for i, x in enumerate(terminal_n) if x, None)
ternimal_index = [i for i, x in enumerate(terminal_n) if x]
if len(ternimal_index):
Vs_prime[ternimal_index] = 0
Qs = re_n + self.gamma * Vs_prime
adv_n = Qs - Vs
if self.standardize_advantages:
adv_n = (adv_n - np.mean(adv_n)) / (np.std(adv_n) + 1e-8)
return adv_n
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_recent_data(batch_size)