class BCAgent(BaseAgent):
def __init__(self, env, agent_params):
super(BCAgent).__init__()
# init vars
self.env = env
self.agent_params = agent_params
# actor/policy
self.actor = MLPPolicySL(
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'],
)
# replay buffer
self.replay_buffer = ReplayBuffer(self.agent_params['max_replay_buffer_size'])
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
# training a BC agent refers to updating its actor using
# the given observations and corresponding action labels
log = self.actor.update(ob_no, ac_na) # HW1: you will modify this
return log
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_random_data(batch_size) # HW1: you will modify this
def save(self, path):
return self.actor.save(path)
class BCAgent(BaseAgent):
def __init__(self, env, agent_params):
super(BCAgent, self).__init__()
# init vars
self.env = env
self.agent_params = agent_params
# actor/policy
self.actor = MLPPolicySL(
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'],
siren=self.agent_params['siren'],
train_separate_offset=self.agent_params['train_separate_params'],
supervision_mode=self.agent_params['supervision_mode'],
offset_learning_rate=self.agent_params['offset_learning_rate'],
auto_cast=self.agent_params['auto_cast'],
gradient_loss_scale=self.agent_params['gradient_loss_scale'],
additional_activation=self.agent_params['additional_activation'],
omega=self.agent_params['omega'])
# replay buffer
self.replay_buffer = ReplayBuffer(
self.agent_params['max_replay_buffer_size'],
epsilon_s=self.agent_params['epsilon_s'])
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n, gradients):
# training a BC agent refers to updating its actor using
# the given observations and corresponding action labels
log = self.actor.update(
ob_no, ac_na, gradients=gradients) # HW1: you will modify this
return log
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_random_data(
batch_size) # HW1: you will modify this
def save(self, path):
return self.actor.save(path)
class BCAgent(BaseAgent):
def __init__(self, env, agent_params):
super(BCAgent, self).__init__()
# init vars
self.env = env
self.agent_params = agent_params
# actor/policy
self.actor = MLPPolicySL(
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'],
)
# replay buffer
self.replay_buffer = ReplayBuffer(
self.agent_params['max_replay_buffer_size'])
def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):
""" Update actor policy by supervised learning,
given observations and action labels.
- ob_no: Observations.
- ac_na: Action lables
- re_n: ?
- next_ob_no: ?
- terminal_n: ?
"""
log = self.actor.update(ob_no, ac_na)
return log
def add_to_replay_buffer(self, paths):
self.replay_buffer.add_rollouts(paths)
def sample(self, batch_size):
return self.replay_buffer.sample_random_data(
batch_size) # HW1: you will modify this
def save(self, path):
return self.actor.save(path)