def replay_sample(self):
'''Samples a batch from memory'''
batches = [body.replay_memory.sample() for body in self.agent.nanflat_body_a]
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.gpu)
assert not torch.isnan(batch['states']).any()
return batch
def replay_sample(self):
'''Samples a batch from memory'''
batches = [
body.replay_memory.sample() for body in self.agent.nanflat_body_a
]
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.gpu)
assert not torch.isnan(batch['states']).any()
return batch
def sample(self):
'''Modify the onpolicy sample to also append to replay'''
batches = [body.memory.sample() for body in self.agent.nanflat_body_a]
batch = util.concat_batches(batches)
data_keys = self.body.replay_memory.data_keys
for idx in range(len(batch['dones'])):
tuples = [batch[k][idx] for k in data_keys]
self.body.replay_memory.add_experience(*tuples)
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def sample(self):
'''Modify the onpolicy sample to also append to replay'''
batches = [body.memory.sample() for body in self.agent.nanflat_body_a]
batch = util.concat_batches(batches)
data_keys = self.body.replay_memory.data_keys
for idx in range(len(batch['dones'])):
tuples = [batch[k][idx] for k in data_keys]
self.body.replay_memory.add_experience(*tuples)
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def space_sample(self):
'''Samples a batch from memory'''
batches = []
for body in self.agent.nanflat_body_a:
self.body = body
batches.append(self.sample())
# set body reference back to default
self.body = self.agent.nanflat_body_a[0]
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.device, self.body.memory.is_episodic)
return batch
def sample(self):
'''Samples a batch from memory of size self.memory_spec['batch_size']'''
batches = []
for body in self.agent.nanflat_body_a:
body_batch = body.memory.sample()
# one-hot actions to calc q_targets
if body.is_discrete:
body_batch['actions'] = util.to_one_hot(body_batch['actions'], body.action_space.high)
batches.append(body_batch)
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def sample(self):
'''Samples a batch from memory'''
batches = []
for body in self.agent.nanflat_body_a:
body_batch = body.memory.sample()
# one-hot actions to calc q_targets
if body.is_discrete:
body_batch['one_hot_actions'] = util.to_one_hot(body_batch['actions'], body.action_space.high)
batches.append(body_batch)
batch = util.concat_batches(batches)
# this is safe for next_action at done since the calculated act_next_q_preds will be multiplied by (1 - batch['dones'])
batch['next_actions'] = np.zeros_like(batch['actions'])
batch['next_actions'][:-1] = batch['actions'][1:]
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def sample(self):
'''Samples a batch from memory'''
batches = []
for body in self.agent.nanflat_body_a:
body_batch = body.memory.sample()
# one-hot actions to calc q_targets
if body.is_discrete:
body_batch['one_hot_actions'] = util.to_one_hot(body_batch['actions'], body.action_space.high)
batches.append(body_batch)
batch = util.concat_batches(batches)
# this is safe for next_action at done since the calculated act_next_q_preds will be multiplied by (1 - batch['dones'])
batch['next_actions'] = np.zeros_like(batch['actions'])
batch['next_actions'][:-1] = batch['actions'][1:]
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def sample(self):
'''Samples a batch from memory'''
batches = [body.memory.sample() for body in self.agent.nanflat_body_a]
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
def sample(self):
'''Samples a batch from memory'''
batches = [body.memory.sample() for body in self.agent.nanflat_body_a]
batch = util.concat_batches(batches)
batch = util.to_torch_batch(batch, self.net.gpu)
return batch
