if __name__ == '__main__':
os.environ['OMP_NUM_THREADS'] = '1'
args = parser.parse_args()
env = gym.make("FetchPickAndPlace-v1")
shared_model = Actor()
if args.use_cuda:
shared_model.cuda()
torch.cuda.manual_seed_all(30)
shared_model.share_memory()
if os.path.isfile(args.save_path1):
print('Loading A3C parametets ...')
pretrained_dict = torch.load(args.save_path1)
model_dict = shared_model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
shared_model.load_state_dict(model_dict)
optimizer = SharedAdam(shared_model.parameters(), lr=args.lr)
optimizer.share_memory()
for p in shared_model.fc1.parameters():
p.requires_grad = False
for p in shared_model.fc2.parameters():
p.requires_grad = False
class DDPG:
def __init__(self, env):
self.env = env
self.stateDim = obs2state(env.reset().observation).size()[1]
self.actionDim = env.action_spec().shape[0]
self.actor = Actor(self.env).cuda()
self.critic = Critic(self.env).cuda()
self.targetActor = deepcopy(Actor(self.env)).cuda()
self.targetCritic = deepcopy(Critic(self.env)).cuda()
self.actorOptim = optim.Adam(self.actor.parameters(), lr=ACTOR_LR)
self.criticOptim = optim.Adam(self.critic.parameters(), lr=CRITIC_LR)
self.criticLoss = nn.MSELoss()
self.noise = OUNoise(mu=np.zeros(self.actionDim), sigma=SIGMA)
self.replayBuffer = Buffer(BUFFER_SIZE)
self.batchSize = MINIBATCH_SIZE
self.checkpoint_dir = CHECKPOINT_DIR
self.discount = DISCOUNT
self.warmup = WARMUP
self.epsilon = EPSILON
self.epsilon_decay = EPSILON_DECAY
self.rewardgraph = []
self.start = 0
self.end = NUM_EPISODES
def getQTarget(self, nextStateBatch, rewardBatch, terminalBatch):
"""Inputs: Batch of next states, rewards and terminal flags of size self.batchSize
Calculates the target Q-value from reward and bootstraped Q-value of next state
using the target actor and target critic
Outputs: Batch of Q-value targets"""
targetBatch = torch.FloatTensor(rewardBatch).cuda()
nonFinalMask = torch.ByteTensor(
tuple(map(lambda s: s != True, terminalBatch)))
nextStateBatch = torch.cat(nextStateBatch)
nextActionBatch = self.targetActor(nextStateBatch)
nextActionBatch.volatile = True
qNext = self.targetCritic(nextStateBatch, nextActionBatch)
nonFinalMask = self.discount * nonFinalMask.type(
torch.cuda.FloatTensor)
targetBatch += nonFinalMask * qNext.squeeze().data
return Variable(targetBatch, volatile=False)
def updateTargets(self, target, original):
"""Weighted average update of the target network and original network
Inputs: target actor(critic) and original actor(critic)"""
for targetParam, orgParam in zip(target.parameters(),
original.parameters()):
targetParam.data.copy_((1 - TAU)*targetParam.data + \
TAU*orgParam.data)
def getMaxAction(self, curState):
"""Inputs: Current state of the episode
Returns the action which maximizes the Q-value of the current state-action pair"""
spec = self.env.action_spec()
minAct = Variable(torch.cuda.FloatTensor(spec.minimum),
requires_grad=False)
maxAct = Variable(torch.cuda.FloatTensor(spec.maximum),
requires_grad=False)
noise = self.epsilon * Variable(torch.FloatTensor(self.noise()),
volatile=True).cuda()
action = self.actor(curState)
actionNoise = action + noise
return actionNoise
def train(self):
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
print('Training started...')
for i in range(self.start, self.end):
time_step = self.env.reset()
ep_reward = 0
while not time_step.last():
#Visualize Training
display.clear_output(wait=True)
plt.imshow(self.env.physics.render())
plt.show()
# Get maximizing action
curState = Variable(obs2state(time_step.observation),
volatile=True).cuda()
self.actor.eval()
action = self.getMaxAction(curState)
curState.volatile = False
action.volatile = False
self.actor.train()
# Step episode
time_step = self.env.step(action.data)
nextState = Variable(obs2state(time_step.observation),
volatile=True).cuda()
reward = time_step.reward
ep_reward += reward
terminal = time_step.last()
# Update replay bufer
self.replayBuffer.append(
(curState, action, nextState, reward, terminal))
# Training loop
if len(self.replayBuffer) >= self.warmup:
curStateBatch, actionBatch, nextStateBatch, \
rewardBatch, terminalBatch = self.replayBuffer.sample_batch(self.batchSize)
curStateBatch = torch.cat(curStateBatch)
actionBatch = torch.cat(actionBatch)
qPredBatch = self.critic(curStateBatch, actionBatch)
qTargetBatch = self.getQTarget(nextStateBatch, rewardBatch,
terminalBatch)
# Critic update
self.criticOptim.zero_grad()
criticLoss = self.criticLoss(qPredBatch, qTargetBatch)
print('Critic Loss: {}'.format(criticLoss))
criticLoss.backward()
self.criticOptim.step()
# Actor update
self.actorOptim.zero_grad()
actorLoss = -torch.mean(
self.critic(curStateBatch, self.actor(curStateBatch)))
print('Actor Loss: {}'.format(actorLoss))
actorLoss.backward()
self.actorOptim.step()
# Update Targets
self.updateTargets(self.targetActor, self.actor)
self.updateTargets(self.targetCritic, self.critic)
self.epsilon -= self.epsilon_decay
if i % 20 == 0:
self.save_checkpoint(i)
self.rewardgraph.append(ep_reward)
def save_checkpoint(self, episode_num):
checkpointName = self.checkpoint_dir + 'ep{}.pth.tar'.format(
episode_num)
checkpoint = {
'episode': episode_num,
'actor': self.actor.state_dict(),
'critic': self.critic.state_dict(),
'targetActor': self.targetActor.state_dict(),
'targetCritic': self.targetCritic.state_dict(),
'actorOpt': self.actorOptim.state_dict(),
'criticOpt': self.criticOptim.state_dict(),
'replayBuffer': self.replayBuffer,
'rewardgraph': self.rewardgraph,
'epsilon': self.epsilon
}
torch.save(checkpoint, checkpointName)
def loadCheckpoint(self, checkpointName):
if os.path.isfile(checkpointName):
print("Loading checkpoint...")
checkpoint = torch.load(checkpointName)
self.start = checkpoint['episode'] + 1
self.actor.load_state_dict(checkpoint['actor'])
self.critic.load_state_dict(checkpoint['critic'])
self.targetActor.load_state_dict(checkpoint['targetActor'])
self.targetCritic.load_state_dict(checkpoint['targetCritic'])
self.actorOptim.load_state_dict(checkpoint['actorOpt'])
self.criticOptim.load_state_dict(checkpoint['criticOpt'])
self.replayBuffer = checkpoint['replayBuffer']
self.rewardgraph = checkpoint['rewardgraph']
self.epsilon = checkpoint['epsilon']
print('Checkpoint loaded')
else:
raise OSError('Checkpoint not found')
