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ddpg.py
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import numpy as np
from copy import deepcopy
from mushroom_rl.algorithms.actor_critic.deep_actor_critic import DeepAC
from mushroom_rl.approximators import Regressor
from mushroom_rl.approximators.parametric import TorchApproximator
from utils import normalize_and_clip
class DDPG(DeepAC):
def __init__(self, mdp_info, policy_class, policy_params,
actor_params, actor_optimizer, critic_params, batch_size,
replay_memory, tau, optimization_steps, comm, policy_delay=1,
critic_fit_params=None):
self._critic_fit_params = dict() if critic_fit_params is None else critic_fit_params
self._batch_size = batch_size
self._tau = tau
self._optimization_steps = optimization_steps
self._comm = comm
self._policy_delay = policy_delay
self._fit_count = 0
if comm.Get_rank() == 0:
self._replay_memory = replay_memory
target_critic_params = deepcopy(critic_params)
self._critic_approximator = Regressor(TorchApproximator,
**critic_params)
self._target_critic_approximator = Regressor(TorchApproximator,
**target_critic_params)
target_actor_params = deepcopy(actor_params)
self._actor_approximator = Regressor(TorchApproximator,
**actor_params)
self._target_actor_approximator = Regressor(TorchApproximator,
**target_actor_params)
self._init_target(self._critic_approximator,
self._target_critic_approximator)
self._init_target(self._actor_approximator,
self._target_actor_approximator)
policy = policy_class(self._actor_approximator, **policy_params)
policy_parameters = self._actor_approximator.model.network.parameters()
self._add_save_attr(
_critic_fit_params='pickle',
_batch_size='numpy',
_tau='numpy',
_policy_delay='numpy',
_fit_count='numpy',
_replay_memory='pickle',
_critic_approximator='pickle',
_target_critic_approximator='pickle',
_actor_approximator='pickle',
_target_actor_approximator='pickle'
)
super().__init__(mdp_info, policy, actor_optimizer, policy_parameters)
def fit(self, dataset):
if self._comm.Get_rank() == 0:
for i in range(1, self._comm.Get_size()):
dataset += self._comm.recv(source=i)
self._replay_memory.add(dataset)
self._comm.Barrier()
else:
self._comm.send(dataset, dest=0)
self._comm.Barrier()
for _ in range(self._optimization_steps):
if self._comm.Get_rank() == 0:
state, action, reward, next_state =\
self._replay_memory.get(self._batch_size * self._comm.Get_size())
else:
state = None
action = None
reward = None
next_state = None
state, action, reward, next_state = self._comm.bcast(
[state, action, reward, next_state], root=0
)
start = self._batch_size * self._comm.Get_rank()
stop = start + self._batch_size
state = state[start:stop]
action = action[start:stop]
reward = reward[start:stop]
next_state = next_state[start:stop]
q_next = self._next_q(next_state)
q = reward + self.mdp_info.gamma * q_next
q = np.clip(q, -1 / (1 - self.mdp_info.gamma), 0)
self._critic_approximator.fit(state, action, q,
**self._critic_fit_params)
if self._fit_count % self._policy_delay == 0:
loss = self._loss(state)
self._optimize_actor_parameters(loss)
self._fit_count += 1
self._update_target(self._critic_approximator,
self._target_critic_approximator)
self._update_target(self._actor_approximator,
self._target_actor_approximator)
def _loss(self, state):
action = self._actor_approximator(state, output_tensor=True,
scaled=False)
q = self._critic_approximator(state, action, output_tensor=True)
return -q.mean() + (action ** 2).mean()
def _next_q(self, next_state):
a = self._target_actor_approximator(next_state)
q = self._target_critic_approximator.predict(next_state, a)
return q
def _post_load(self):
if self._optimizer is not None:
self._parameters = list(self._actor_approximator.model.network.parameters())
def draw_action(self, state):
state = np.append(state['observation'], state['desired_goal'])
if self._comm.Get_rank() == 0:
mu = self._replay_memory._mu
sigma2 = self._replay_memory._sigma2
else:
mu = None
sigma2 = None
mu, sigma2 = self._comm.bcast([mu, sigma2], root=0)
if not np.any(sigma2 == 0):
state = normalize_and_clip(state, mu, sigma2)
return self.policy.draw_action(state)