class DDPGAgent(Agent):
''' stevenpjg's implementation of DDPG algorithm '''
REPLAY_MEMORY_SIZE = 10000
BATCH_SIZE = 64
GAMMA = 0.99
def __init__(self,
env,
dir,
is_batch_norm=False,
is_grad_inverter=True,
training_flag=True):
super().__init__(env, dir)
assert isinstance(env.action_space,
Box), "action space must be continuous"
if is_batch_norm:
self.critic_net = CriticNet_bn(self.observation_space_size,
self.action_space_size)
self.actor_net = ActorNet_bn(self.observation_space_size,
self.action_space_size)
else:
self.critic_net = CriticNet(self.observation_space_size,
self.action_space_size)
self.actor_net = ActorNet(self.observation_space_size,
self.action_space_size)
self.is_grad_inverter = is_grad_inverter
self.training_flag = training_flag
self.replay_memory = deque()
self.time_step = 0
action_max = np.array(self.high).tolist()
action_min = np.array(self.low).tolist()
action_bounds = [action_max, action_min]
self.grad_inv = grad_inverter(action_bounds)
self.data_fetch = None
def add_data_fetch(self, df):
self.data_fetch = df
def get_short_name(self):
return 'DDPG'
def act(self, state):
state = self._np_shaping(state, True)
result = self.actor_net.evaluate_actor(state).astype(float)
if self.data_fetch:
self.data_fetch.set_actors_action(result[0].tolist())
return result
def observe(self, episode):
episode['obs'] = self._np_shaping(episode['obs'], True)
episode['action'] = self._np_shaping(episode['action'], False)
episode['obs2'] = self._np_shaping(episode['obs2'], True)
self.add_experience(episode)
def add_experience(self, episode):
self.replay_memory.append(episode)
self.time_step += 1
if len(self.replay_memory) > type(self).REPLAY_MEMORY_SIZE:
self.replay_memory.popleft()
if len(self.replay_memory) > type(self).BATCH_SIZE:
res = self.train()
return res
else:
return None
def minibatches(self):
batch = random.sample(self.replay_memory, type(self).BATCH_SIZE)
# state t
state = self._np_shaping(np.array([item['obs'] for item in batch]),
True)
# action
action = self._np_shaping(np.array([item['action'] for item in batch]),
False)
# reward
reward = np.array([item['reward'] for item in batch])
# state t+1
state_2 = self._np_shaping(np.array([item['obs2'] for item in batch]),
True)
# doneA
done = np.array([item['done'] for item in batch])
return state, action, reward, state_2, done
def train(self):
if not self.training_flag:
return
# sample a random minibatch of N transitions from R
state, action, reward, state_2, done = self.minibatches()
actual_batch_size = len(state)
target_action = self.actor_net.evaluate_target_actor(state)
# Q'(s_i+1,a_i+1)
q_t = self.critic_net.evaluate_target_critic(state_2, target_action)
y = []
for i in range(0, actual_batch_size):
if done[i]:
y.append(reward[i])
else:
y.append(reward[i] +
type(self).GAMMA * q_t[i][0]) # q_t+1 instead of q_t
y = np.reshape(np.array(y), [len(y), 1])
# Update critic by minimizing the loss
self.critic_net.train_critic(state, action, y)
# Update actor proportional to the gradients:
# action_for_delQ = self.act(state) # was self.evaluate_actor instead of self.act
action_for_delQ = self.actor_net.evaluate_actor(
state) # dont need wolp action
if self.is_grad_inverter:
del_Q_a = self.critic_net.compute_delQ_a(
state, action_for_delQ) # /BATCH_SIZE
del_Q_a = self.grad_inv.invert(del_Q_a, action_for_delQ)
else:
del_Q_a = self.critic_net.compute_delQ_a(
state, action_for_delQ)[0] # /BATCH_SIZE
# train actor network proportional to delQ/dela and del_Actor_model/del_actor_parameters:
self.actor_net.train_actor(state, del_Q_a)
# Update target Critic and actor network
self.critic_net.update_target_critic()
self.actor_net.update_target_actor()
def save_agent(self, force=False, comment="default"):
path = "{}/weights/{}".format(self.get_dir(), comment)
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
print("Saving agent in", path)
self.actor_net.save_model(path + '/actor.ckpt')
self.critic_net.save_model(path + '/critic.ckpt')
else:
if force:
print("Overwrite old agent in", path)
self.actor_net.save_model(path + '/actor.ckpt')
self.critic_net.save_model(path + '/critic.ckpt')
else:
print("Save aborted. An agent is already saved in ", path)
def load_agent(self, agent_name=None, comment="default"):
if agent_name is None:
path = "{}/weights/{}".format(self.get_dir(), comment)
else:
path = "{}/{}/{}/weights/{}".format(self.result_dir, agent_name,
self.env.spec.id, comment)
if os.path.exists(path):
print("Loading agent saved in", path)
self.actor_net.load_model(path + '/actor.ckpt')
self.critic_net.load_model(path + '/critic.ckpt')
else:
print("Agent not found in", path)
def close_session(self):
self.actor_net.close()
self.critic_net.close()
class DDPG:
""" Deep Deterministic Policy Gradient Algorithm"""
def __init__(self,env, is_batch_norm=False):
self.env = env
self.num_states = env.observation_space.shape[0]
self.num_actions = env.action_space.shape[0]
if is_batch_norm:
self.critic_net = CriticNet_bn(self.num_states, self.num_actions)
self.actor_net = ActorNet_bn(self.num_states, self.num_actions)
else:
self.critic_net = CriticNet(self.num_states, self.num_actions)
self.actor_net = ActorNet(self.num_states, self.num_actions)
#Initialize Buffer Network:
self.replay_memory = deque()
#Intialize time step:
self.time_step = 0
self.counter = 0
action_max = np.array(env.action_space.high).tolist()
action_min = np.array(env.action_space.low).tolist()
action_bounds = [action_max,action_min]
self.grad_inv = grad_inverter(action_bounds)
def load_model(self):
try:
self.critic_net.load_model()
self.actor_net.load_model()
except Exception as ex:
print(ex)
def save_model(self):
self.critic_net.save_model()
self.actor_net.save_model()
def evaluate_actor(self, state_t):
return self.actor_net.evaluate_actor(state_t)
def add_experience(self, observation_1, observation_2, action, reward, done):
self.observation_1 = observation_1
self.observation_2 = observation_2
self.action = action
self.reward = reward
self.done = done
self.replay_memory.append((self.observation_1, self.observation_2, self.action, self.reward,self.done))
self.time_step = self.time_step + 1
if(len(self.replay_memory)>REPLAY_MEMORY_SIZE):
self.replay_memory.popleft()
def minibatches(self):
batch = random.sample(self.replay_memory, BATCH_SIZE)
#state t
self.state_t_batch = [item[0] for item in batch]
self.state_t_batch = np.array(self.state_t_batch)
#state t+1
self.state_t_1_batch = [item[1] for item in batch]
self.state_t_1_batch = np.array( self.state_t_1_batch)
self.action_batch = [item[2] for item in batch]
self.action_batch = np.array(self.action_batch)
self.action_batch = np.reshape(self.action_batch,[len(self.action_batch),self.num_actions])
self.reward_batch = [item[3] for item in batch]
self.reward_batch = np.array(self.reward_batch)
self.done_batch = [item[4] for item in batch]
self.done_batch = np.array(self.done_batch)
def train(self):
#sample a random minibatch of N transitions from R
self.minibatches()
self.action_t_1_batch = self.actor_net.evaluate_target_actor(self.state_t_1_batch)
#Q'(s_i+1,a_i+1)
q_t_1 = self.critic_net.evaluate_target_critic(self.state_t_1_batch,self.action_t_1_batch)
self.y_i_batch=[]
for i in range(0,BATCH_SIZE):
if self.done_batch[i]:
self.y_i_batch.append(self.reward_batch[i])
else:
self.y_i_batch.append(self.reward_batch[i] + GAMMA*q_t_1[i][0])
self.y_i_batch=np.array(self.y_i_batch)
self.y_i_batch = np.reshape(self.y_i_batch,[len(self.y_i_batch),1])
# Update critic by minimizing the loss
self.critic_net.train_critic(self.state_t_batch, self.action_batch,self.y_i_batch)
# Update actor proportional to the gradients:
action_for_delQ = self.evaluate_actor(self.state_t_batch)
if is_grad_inverter:
self.del_Q_a = self.critic_net.compute_delQ_a(self.state_t_batch,action_for_delQ)#/BATCH_SIZE
self.del_Q_a = self.grad_inv.invert(self.del_Q_a,action_for_delQ)
else:
self.del_Q_a = self.critic_net.compute_delQ_a(self.state_t_batch,action_for_delQ)[0]#/BATCH_SIZE
# train actor network proportional to delQ/dela and del_Actor_model/del_actor_parameters:
self.actor_net.train_actor(self.state_t_batch,self.del_Q_a)
# Update target Critic and actor network
self.critic_net.update_target_critic()
self.actor_net.update_target_actor()
