class CustomTFRPGModel(TFModelV2):
"""Example of interpreting repeated observations."""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super().__init__(obs_space, action_space, num_outputs, model_config,
name)
self.model = TFFCNet(obs_space, action_space, num_outputs,
model_config, name)
self.register_variables(self.model.variables())
def forward(self, input_dict, state, seq_lens):
# The unpacked input tensors, where M=MAX_PLAYERS, N=MAX_ITEMS:
# {
# 'items', <tf.Tensor shape=(?, M, N, 5)>,
# 'location', <tf.Tensor shape=(?, M, 2)>,
# 'status', <tf.Tensor shape=(?, M, 10)>,
# }
print("The unpacked input tensors:", input_dict["obs"])
print()
print("Unbatched repeat dim", input_dict["obs"].unbatch_repeat_dim())
print()
if tf.executing_eagerly():
print("Fully unbatched", input_dict["obs"].unbatch_all())
print()
return self.model.forward(input_dict, state, seq_lens)
def value_function(self):
return self.model.value_function()
class CustomModel(TFModelV2):
"""Example of a keras custom model that just delegates to an fc-net."""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super(CustomModel, self).__init__(obs_space, action_space, num_outputs,
model_config, name)
self.model = FullyConnectedNetwork(obs_space, action_space,
num_outputs, model_config, name)
def forward(self, input_dict, state, seq_lens):
return self.model.forward(input_dict, state, seq_lens)
def value_function(self):
return self.model.value_function()
class CustomModel(TFModelV2):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super().__init__(obs_space, action_space, num_outputs, model_config,
name)
self.model = FullyConnectedNetwork(obs_space, action_space,
num_outputs, model_config, name)
self.register_variables(self.model.variables())
def forward(self, input_dict, state, seq_lens):
return self.model.forward(input_dict, state, seq_lens)
def value_function(self):
return self.model.value_function()
class CentralizedCriticModel(TFModelV2):
"""Multi-agent model that implements a centralized value function."""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super(CentralizedCriticModel,
self).__init__(obs_space, action_space, num_outputs,
model_config, name)
# Base of the model
self.model = FullyConnectedNetwork(obs_space, action_space,
num_outputs, model_config, name)
# Central VF maps (obs, opp_obs, opp_act) -> vf_pred
obs = tf.keras.layers.Input(shape=(6, ), name="obs")
opp_obs = tf.keras.layers.Input(shape=(6, ), name="opp_obs")
opp_act = tf.keras.layers.Input(shape=(2, ), name="opp_act")
concat_obs = tf.keras.layers.Concatenate(axis=1)(
[obs, opp_obs, opp_act])
central_vf_dense = tf.keras.layers.Dense(16,
activation=tf.nn.tanh,
name="c_vf_dense")(concat_obs)
central_vf_out = tf.keras.layers.Dense(
1, activation=None, name="c_vf_out")(central_vf_dense)
self.central_vf = tf.keras.Model(inputs=[obs, opp_obs, opp_act],
outputs=central_vf_out)
@override(ModelV2)
def forward(self, input_dict, state, seq_lens):
return self.model.forward(input_dict, state, seq_lens)
def central_value_function(self, obs, opponent_obs, opponent_actions):
return tf.reshape(
self.central_vf([
obs, opponent_obs,
tf.one_hot(tf.cast(opponent_actions, tf.int32), 2)
]),
[-1],
)
@override(ModelV2)
def value_function(self):
return self.model.value_function() # not used
class CustomModel(TFModelV2, selection_DelayedImpactEnv):
"""Example of a keras custom model that just delegates to an fc-net."""
obs_space = selection_DelayedImpactEnv.observation_space
action_space = selection_DelayedImpactEnv.action_space
# num_outputs=169
model_config = {}
name = 'My_model'
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super(CustomModel, self).__init__(obs_space, action_space, num_outputs,
model_config, name)
self.model = FullyConnectedNetwork(obs_space, action_space,
num_outputs, model_config, name)
self.register_variables(self.model.variables())
def forward(self, input_dict, state, seq_lens):
return self.model.forward(input_dict, state, seq_lens)
def value_function(self):
return self.model.value_function()
