def __init__(self, state_dim, action_dim, config):
super().__init__(state_dim, action_dim, config)
self.network = DDPGBulletNetworkSURND(state_dim, action_dim, config)
self.memory = ExperienceReplayBuffer(config.memory_size)
self.motivation = MetaCriticRNDMotivation(self.network.metacritic_model, config.motivation_lr, config.motivation_variant, config.motivation_eta, self.memory, config.motivation_batch_size,
config.device)
self.algorithm = DDPG(self.network, config.actor_lr, config.critic_lr, config.gamma, config.tau, self.memory, config.batch_size, self.motivation)
def __init__(self, gate, critic, gate_lr, critic_lr, gamma, tau,
memory_buffer, sample_size, forward_model, meta_critic):
self._gate = gate
self._ddpg = DDPG(gate, critic, gate_lr, critic_lr, gamma, tau,
memory_buffer, sample_size)
self._forward_model = forward_model
self._meta_critic = meta_critic
def __init__(self, state_dim, action_dim, config):
super().__init__(state_dim, action_dim, config)
self.network = DDPGAerisNetworkFIM(state_dim, action_dim, config)
self.memory = ExperienceReplayBuffer(config.memory_size)
self.motivation = ForwardInverseModelMotivation(self.network.forward_model, config.forward_model_lr, self.network.inverse_model, config.forward_model_lr,
0.5, config.forward_model_eta,
config.forward_model_variant, self.memory, config.forward_model_batch_size, config.device)
self.algorithm = DDPG(self.network, config.actor_lr, config.critic_lr, config.gamma, config.tau, self.memory, config.batch_size, self.motivation)
def __init__(self, network, lr, gamma, tau, eta, memory, sample_size):
self.network = network
self._optimizer = torch.optim.Adam(self.network.forward_model.parameters(), lr=lr)
self.eta = eta
self._memory = memory
self._sample_size = sample_size
self._weight = torch.tensor([[0.9, 0.1]], dtype=torch.float32)
self.gate_algorithm = DDPG(self.network.gate, lr, lr, gamma, tau, memory, 32)
def __init__(self, state_dim, action_dim, config):
super().__init__(state_dim, action_dim, config)
self.network = DDPGBulletNetwork(state_dim, action_dim, config)
self.memory = ExperienceReplayBuffer(config.memory_size)
self.algorithm = DDPG(self.network, config.actor_lr, config.critic_lr, config.gamma, config.tau, self.memory, config.batch_size)
def __init__(self, state_dim, action_dim, config):
super().__init__(state_dim, action_dim, config)
self.network = DDPGAerisNetworkQRND(state_dim, action_dim, config)
self.memory = ExperienceReplayBuffer(config.memory_size)
self.motivation = QRNDMotivation(self.network.qrnd_model, config.forward_model_lr, config.forward_model_eta, self.memory)
self.algorithm = DDPG(self.network, config.actor_lr, config.critic_lr, config.gamma, config.tau, self.memory, config.batch_size, self.motivation)
def __init__(self, state_dim, action_dim, config):
super().__init__(state_dim, action_dim, config)
self.network = DDPGBulletNetworkDOP(state_dim, action_dim, config)
self.memory = DOPReplayBuffer(config.memory_size)
self.motivation = DOPMotivation(self.network.dop_model, config.motivation_lr, config.motivation_eta, self.memory, config.motivation_batch_size, config.device)
self.algorithm = DDPG(self.network, config.actor_lr, config.critic_lr, config.gamma, config.tau, self.memory, config.batch_size)
int(time.time()))).make()
else:
environment = gym.make(args.env)
is_env_pool = False
tf.reset_default_graph()
with tf.device(device):
if args.eval:
replay_memory = None
else:
replay_memory = Replay_Memory(memory_size=args.memory_size)
if args.model == 'DDPG':
agent = DDPG(environment,
args.hidden_dims,
replay_memory=replay_memory,
gamma=args.gamma,
actor_lr=args.actor_lr,
critic_lr=args.critic_lr,
tau=args.tau,
N=args.N)
elif args.model == 'QRDDPG':
agent = QRDDPG(environment,
args.hidden_dims,
replay_memory=replay_memory,
gamma=args.gamma,
actor_lr=args.actor_lr,
critic_lr=args.critic_lr,
tau=args.tau,
N=args.N,
kappa=args.kappa,
n_quantile=args.n_quantile)
elif args.model == 'D3PG':
import numpy as np
import time
MAX_EPISODES = 900
MAX_EP_STEPS = 200
ON_TRAIN = False
# set env
env = ArmEnv()
s_dim = env.state_dim
a_dim = env.action_dim
a_bound = env.action_bound
# set RL method
pd = PD()
rl = DDPG(a_dim, s_dim, a_bound)
steps = []
def runpd():
for i in range(MAX_EPISODES):
s = env.reset()
ep_r = 0.
for j in range(MAX_EP_STEPS):
a = pd.cal(s, np.array([0, 0, -4, 0, 0]))
s, r, done, safe = env.step(a)
if done or j == MAX_EP_STEPS - 1 or safe is False:
print('Ep: %i | %s | %s | ep_r: %.1f | step: %i' %
(i, '---' if not done else 'done',