class ParametricActionsModel(DistributionalQTFModel):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, **kw):
print("{} : [INFO] ParametricActionsModel {}, {}, {}, {}, {}".format(
datetime.now(), action_space, obs_space, num_outputs, name,
model_config))
super(ParametricActionsModel,
self).__init__(obs_space, action_space, num_outputs,
model_config, name, **kw)
# print("####### obs_space {}".format(obs_space))
# raise Exception("END")
self.action_param_model = FullyConnectedNetwork(
FLAT_OBSERVATION_SPACE, action_space, num_outputs, model_config,
name + "_action_param")
self.register_variables(self.action_param_model.variables())
def forward(self, input_dict, state, seq_lens):
# Extract the available actions tensor from the observation.
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
action_param, _ = self.action_param_model(
{"obs": input_dict["obs"]["state"]})
# Mask out invalid actions (use tf.float32.min for stability)
inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
return action_param + inf_mask, state
def value_function(self):
return self.action_param_model.value_function()
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 ParametricActionsModel(DistributionalQTFModel):
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name,
true_obs_shape=(2, ),
**kw):
super(ParametricActionsModel,
self).__init__(obs_space, action_space, num_outputs,
model_config, name, **kw)
self.action_value_model = FullyConnectedNetwork(
Box(-1, 1, shape=true_obs_shape),
action_space,
num_outputs,
model_config,
name + "_action_values",
)
self.register_variables(self.action_value_model.variables())
def forward(self, input_dict, state, seq_lens):
action_mask = input_dict["obs"]["action_mask"]
action_values, _ = self.action_value_model(
{"obs": input_dict["obs"]["actual_obs"]})
inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
return action_values + inf_mask, state
class FCMaskedActionsModelTF(DistributionalQTFModel, TFModelV2):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, **kw):
super(FCMaskedActionsModelTF,
self).__init__(obs_space, action_space, num_outputs,
model_config, name, **kw)
true_obs_space = gym.spaces.MultiBinary(n=obs_space.shape[0] -
action_space.n)
self.action_embed_model = FullyConnectedNetwork(
obs_space=true_obs_space,
action_space=action_space,
num_outputs=action_space.n,
model_config=model_config,
name=name + "action_model")
self.register_variables(self.action_embed_model.variables())
def forward(self, input_dict, state, seq_lens):
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
raw_actions, _ = self.action_embed_model(
{"obs": input_dict["obs"]["real_obs"]})
#inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
logits = tf.where(tf.math.equal(action_mask, 1), raw_actions,
tf.float32.min)
return logits, state
def value_function(self):
return self.action_embed_model.value_function()
class YetAnotherCentralizedCriticModel(TFModelV2):
"""Multi-agent model that implements a centralized value function.
It assumes the observation is a dict with 'own_obs' and 'opponent_obs', the
former of which can be used for computing actions (i.e., decentralized
execution), and the latter for optimization (i.e., centralized learning).
This model has two parts:
- An action model that looks at just 'own_obs' to compute actions
- A value model that also looks at the 'opponent_obs' / 'opponent_action'
to compute the value (it does this by using the 'obs_flat' tensor).
"""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super(YetAnotherCentralizedCriticModel, self).__init__(
obs_space, action_space, num_outputs, model_config, name)
self.action_model = FullyConnectedNetwork(
Box(low=0, high=1, shape=(6, )), # one-hot encoded Discrete(6)
action_space,
num_outputs,
model_config,
name + "_action")
self.register_variables(self.action_model.variables())
self.value_model = FullyConnectedNetwork(obs_space, action_space, 1,
model_config, name + "_vf")
self.register_variables(self.value_model.variables())
def forward(self, input_dict, state, seq_lens):
self._value_out, _ = self.value_model({
"obs": input_dict["obs_flat"]
}, state, seq_lens)
return self.action_model({
"obs": input_dict["obs"]["own_obs"]
}, state, seq_lens)
def value_function(self):
return tf.reshape(self._value_out, [-1])
class CustomLossModel(TFModelV2):
"""Custom model that adds an imitation loss on top of the policy loss."""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
super().__init__(obs_space, action_space, num_outputs, model_config,
name)
self.fcnet = FullyConnectedNetwork(
self.obs_space,
self.action_space,
num_outputs,
model_config,
name="fcnet")
self.register_variables(self.fcnet.variables())
@override(ModelV2)
def forward(self, input_dict, state, seq_lens):
# Delegate to our FCNet.
return self.fcnet(input_dict, state, seq_lens)
@override(ModelV2)
def custom_loss(self, policy_loss, loss_inputs):
# Create a new input reader per worker.
reader = JsonReader(
self.model_config["custom_model_config"]["input_files"])
input_ops = reader.tf_input_ops()
# Define a secondary loss by building a graph copy with weight sharing.
obs = restore_original_dimensions(
tf.cast(input_ops["obs"], tf.float32), self.obs_space)
logits, _ = self.forward({"obs": obs}, [], None)
# You can also add self-supervised losses easily by referencing tensors
# created during _build_layers_v2(). For example, an autoencoder-style
# loss can be added as follows:
# ae_loss = squared_diff(
# loss_inputs["obs"], Decoder(self.fcnet.last_layer))
print("FYI: You can also use these tensors: {}, ".format(loss_inputs))
# Compute the IL loss.
action_dist = Categorical(logits, self.model_config)
self.policy_loss = policy_loss
self.imitation_loss = tf.reduce_mean(
-action_dist.logp(input_ops["actions"]))
return policy_loss + 10 * self.imitation_loss
def custom_stats(self):
return {
"policy_loss": self.policy_loss,
"imitation_loss": self.imitation_loss,
}
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 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)
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 ParametricActionsModel(DistributionalQTFModel):
"""Parametric action model that handles the dot product and masking.
This assumes the outputs are logits for a single Categorical action dist.
Getting this to work with a more complex output (e.g., if the action space
is a tuple of several distributions) is also possible but left as an
exercise to the reader.
"""
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name,
true_obs_shape=(4, ),
action_embed_size=2,
**kw):
super(ParametricActionsModel, self).__init__(
obs_space, action_space, num_outputs, model_config, name, **kw)
self.action_embed_model = FullyConnectedNetwork(
Box(-1, 1, shape=true_obs_shape), action_space, action_embed_size,
model_config, name + "_action_embed")
self.register_variables(self.action_embed_model.variables())
def forward(self, input_dict, state, seq_lens):
# Extract the available actions tensor from the observation.
avail_actions = input_dict["obs"]["avail_actions"]
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
action_embed, _ = self.action_embed_model({
"obs": input_dict["obs"]["cart"]
})
# Expand the model output to [BATCH, 1, EMBED_SIZE]. Note that the
# avail actions tensor is of shape [BATCH, MAX_ACTIONS, EMBED_SIZE].
intent_vector = tf.expand_dims(action_embed, 1)
# Batch dot product => shape of logits is [BATCH, MAX_ACTIONS].
action_logits = tf.reduce_sum(avail_actions * intent_vector, axis=2)
# Mask out invalid actions (use tf.float32.min for stability)
inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
return action_logits + inf_mask, state
def value_function(self):
return self.action_embed_model.value_function()
class OwnershipActionMaskingModel(FullyConnectedNetwork):
"""
Parametric action model that handles the dot product and masking.
This assumes the outputs are logits for a single Categorical action dist.
"""
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, **kw):
super(OwnershipActionMaskingModel,
self).__init__(obs_space, action_space, num_outputs,
model_config, name, **kw)
self.true_obs_shape = model_config['custom_model_config'][
'true_obs_shape']
self.action_embed_size = model_config['custom_model_config'][
'action_embed_size']
self.action_embed_model = FullyConnectedNetwork(
self.true_obs_shape, action_space, self.action_embed_size,
model_config, name + "_action_embed")
# Box(-1, 0, shape=true_obs_shape)
self.register_variables(self.action_embed_model.variables())
def forward(self, input_dict, state, seq_lens):
# Extract the available actions tensor from the observation.
avail_actions = input_dict["obs"]["avail_actions"]
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
action_embed, _ = self.action_embed_model(
{"obs": input_dict["obs"]["obs"]})
# Expand the model output to [BATCH, 1, EMBED_SIZE]. Note that the
# avail actions tensor is of shape [BATCH, MAX_ACTIONS, EMBED_SIZE].
intent_vector = tf.expand_dims(action_embed, 1)
# Batch dot product => shape of logits is [BATCH, MAX_ACTIONS].
action_logits = tf.reduce_sum(avail_actions * intent_vector, axis=1)
# Mask out invalid actions (use tf.float32.min for stability)
inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
return action_logits + inf_mask, state
def value_function(self):
return self.action_embed_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)
self.register_variables(self.model.variables())
# 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)
self.register_variables(self.central_vf.variables)
@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 KP0ActionMaskModel(TFModelV2):
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name,
true_obs_shape=(11, ),
action_embed_size=5,
*args,
**kwargs):
super(KP0ActionMaskModel,
self).__init__(obs_space, action_space, num_outputs,
model_config, name, *args, **kwargs)
self.action_embed_model = FullyConnectedNetwork(
CatanatronEnv.observation_space,
action_space,
action_embed_size,
model_config,
name + "_action_embedding",
)
self.register_variables(self.action_embed_model.variables())
def forward(self, input_dict, state, seq_lens):
avail_actions = input_dict["obs"]["avail_actions"]
action_mask = input_dict["obs"]["action_mask"]
action_embedding, _ = self.action_embed_model(
{"obs": input_dict["obs"]["state"]})
intent_vector = tf.expand_dims(action_embedding, 1)
action_logits = tf.reduce_sum(avail_actions * intent_vector, axis=1)
inf_mask = tf.maximum(tf.log(action_mask), tf.float32.min)
return action_logits + inf_mask, state
def value_function(self):
return self.action_embed_model.value_function()
