DDPGModel

DDPGModel(
   lr = 0.001, tau = 0.0001, gamma = 0.99, in_features = 1, out_features = 1
)

---

Deep Deterministic Policy Gradient

Arguments

actor_model (nn.Model instance): See Model for details. critic_model (nn.Model instance): See Model for details. optimizer (optimizers.Optimizer instance): See Optimizer for details. action_inp (layers.Input / layers.InputLayer instance): See Input for details. tau (float): tau. gamma (float): gamma.

Methods:

.load

.load(
   path = ''
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.save

.save(
   path = ''
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.initialize

.initialize(
   w_init = None, b_init = None
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.softupdate

.softupdate(
   network: str
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.hardupdate

.hardupdate(
   network: str
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.predict

.predict(
   state = None
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.train

.train(
   batch = None, update_target = True
)

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.

.evaluate

.evaluate()

---

Remember the transaction.

Accepts a state, action, reward, next_state, terminal transaction.

Arguments

transaction (abstract): state, action, reward, next_state, terminal transaction.