def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.num_critic_updates_per_agent_update = agent_params[
'num_critic_updates_per_agent_update']
self.num_actor_updates_per_agent_update = agent_params[
'num_actor_updates_per_agent_update']
self.device = agent_params['device']
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['device'],
discrete=self.agent_params['discrete'],
learning_rate=self.agent_params['learning_rate'],
)
# introduced in actor-critic to improve advantage function.
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super().__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.n_drivers = self.agent_params['n_drivers']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size_ac'],
self.agent_params['shared_exp'],
self.agent_params['shared_exp_lambda'],
self.agent_params['is_city'],
self.agent_params['learning_rate'],
self.agent_params['n_drivers']
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params, **kwargs):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
# actor/policy
if self.agent_params['discrete']:
self.actor = DiscreteMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
else:
self.actor = ContinuousMLPPolicy(self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'])
self.policy_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.critic_loss = tf.keras.losses.MeanSquaredError()
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=self.agent_params['learning_rate'])
self.critic.nn_critic.compile(optimizer=self.critic_optimizer, loss=self.critic_loss)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params['standardize_advantages']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params["gamma"]
self.standardize_advantages = self.agent_params["standardize_advantages"]
self.actor = MLPPolicyAC(
self.agent_params["ac_dim"],
self.agent_params["ob_dim"],
self.agent_params["n_layers"],
self.agent_params["size"],
self.agent_params["discrete"],
self.agent_params["learning_rate"],
)
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()
def __init__(self, env, agent_params):
super(ACAgent, self).__init__()
self.env = env
self.agent_params = agent_params
self.gamma = self.agent_params['gamma']
self.standardize_advantages = self.agent_params[
'standardize_advantages']
self.gae = self.agent_params['gae']
self.gae_lambda = self.agent_params['gae_lambda']
self.ppo = self.agent_params['ppo']
self.actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
if self.ppo:
self.old_actor = MLPPolicyAC(
self.agent_params['ac_dim'],
self.agent_params['ob_dim'],
self.agent_params['n_layers'],
self.agent_params['size'],
self.agent_params['discrete'],
self.agent_params['learning_rate'],
self.agent_params['clip_eps'],
)
self.old_actor.load_state_dict(self.actor.state_dict())
self.critic = BootstrappedContinuousCritic(self.agent_params)
self.replay_buffer = ReplayBuffer()