def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
self.action_model = TorchFC(
Box(low=0, high=1, shape=(6, )), # one-hot encoded Discrete(6)
action_space,
num_outputs,
model_config,
name + "_action")
self.value_model = TorchFC(obs_space, action_space, 1, model_config,
name + "_vf")
def __init__(
self,
obs_space,
action_space,
num_outputs,
model_config,
name,
**kwargs,
):
orig_space = getattr(obs_space, "original_space", obs_space)
assert (isinstance(orig_space, Dict)
and "action_mask" in orig_space.spaces
and "observations" in orig_space.spaces)
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kwargs)
nn.Module.__init__(self)
self.internal_model = TorchFC(
orig_space["observations"],
action_space,
num_outputs,
model_config,
name + "_internal",
)
# disable action masking --> will likely lead to invalid actions
self.no_masking = False
if "no_masking" in model_config["custom_model_config"]:
self.no_masking = model_config["custom_model_config"]["no_masking"]
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, custom_input_space, action_space,
num_outputs, model_config, name)
nn.Module.__init__(self)
self.torch_sub_model = TorchFC(custom_input_space, action_space,
num_outputs, model_config, name)
prev_safe_layer_size = int(np.product(custom_input_space.shape))
vf_layers = []
activation = model_config.get("fcnet_activation")
hiddens = [32]
for size in hiddens:
vf_layers.append(
SlimFC(in_size=prev_safe_layer_size,
out_size=size,
activation_fn=activation,
initializer=normc_initializer(1.0)))
prev_safe_layer_size = size
vf_layers.append(
SlimFC(in_size=prev_safe_layer_size,
out_size=1,
initializer=normc_initializer(0.01),
activation_fn=None))
self.safe_branch_separate = nn.Sequential(*vf_layers)
self.last_in = None
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
true_obs_space = Box(low=0, high=1, shape=(266, ), dtype=int)
self.action_model = TorchFC(true_obs_space, action_space, num_outputs,
model_config, name)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
self.torch_sub_model = TorchFC(obs_space, action_space, num_outputs,
model_config, name)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
print(f'CUDA {torch.cuda.is_available()}')
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
self.torch_sub_model = TorchFC(obs_space, action_space, num_outputs,
model_config, name)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, **kwargs):
DQNTorchModel.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kwargs)
true_obs_space = Box(low=0,
high=1,
shape=obs_space.original_space["state"].shape,
dtype=int)
self.action_model = TorchFC(true_obs_space, action_space, num_outputs,
model_config, name)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
true_obs_space = Box(low=0.0,
high=55.0,
shape=(100, ),
dtype=np.float32)
self.torch_sub_model = TorchFC(true_obs_space, action_space,
num_outputs, model_config, name)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
self.torch_sub_model = TorchFC(custom_input_space, action_space,
num_outputs, model_config, name)
self.torch_sub_model._logits = torch.nn.Sequential(
self.torch_sub_model._logits,
torch.nn.Hardtanh(min_val=-3, max_val=3))
def __init__(
self, obs_space, action_space, num_outputs, model_config, name, input_files
):
super().__init__(obs_space, action_space, num_outputs, model_config, name)
nn.Module.__init__(self)
self.input_files = input_files
# Create a new input reader per worker.
self.reader = JsonReader(self.input_files)
self.fcnet = TorchFC(
self.obs_space, self.action_space, num_outputs, model_config, name="fcnet"
)
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, **kw):
DQNTorchModel.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kw)
obs_len = obs_space.shape[0] - action_space.n
orig_obs_space = Box(shape=(obs_len, ),
low=obs_space.low[:obs_len],
high=obs_space.high[:obs_len])
self.action_embed_model = TorchFC(orig_obs_space, action_space,
action_space.n, model_config,
name + "_action_embed")
def __init__(self, obs_space, action_space, num_outputs, model_config,
name, input_files):
super().__init__(obs_space, action_space, num_outputs, model_config,
name)
nn.Module.__init__(self)
self.input_files = input_files
self.fcnet = TorchFC(
self.obs_space,
self.action_space,
num_outputs,
model_config,
name="fcnet")
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
# Base of the model
self.model = TorchFC(obs_space, action_space, num_outputs,
model_config, name)
# Central VF maps (obs, opp_obs, opp_act) -> vf_pred
input_size = 6 + 6 + 2 # obs + opp_obs + opp_act
self.central_vf_dense = SlimFC(input_size, 16, activation_fn=nn.Tanh)
self.central_vf_out = SlimFC(16, 1)
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name,
true_obs_shape=(4, ),
action_embed_size=2,
**kw):
DQNTorchModel.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kw)
self.action_embed_model = TorchFC(
Box(-1, 1, shape=true_obs_shape), action_space, action_embed_size,
model_config, name + "_action_embed")
def __init__(self, obs_space, action_space, num_outputs, model_config, name):
self.custom_model_config = config
# Reshape obs to vector and convert to float
volume = np.prod(obs_space.shape)
space = np.zeros(volume)
flat_observation_space = spaces.Box(low=self.custom_model_config["observation_min"],
high=self.custom_model_config["observation_max"],
shape=space.shape, dtype=np.float32)
# TODO: Transform to output of any other PyTorch and pass new shape to model.
# Create default model (for RL)
TorchModelV2.__init__(self, flat_observation_space, action_space, num_outputs, model_config, name)
nn.Module.__init__(self)
self.torch_sub_model = TorchFC(flat_observation_space, action_space, num_outputs, model_config, name)
def __init__(
self,
obs_space,
action_space,
num_outputs,
model_config,
name,
action_embed_size=2, # Dimensionality of sentence embeddings TODO don't make this hard-coded
**kw):
DQNTorchModel.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kw)
self.true_obs_preprocessor = DictFlatteningPreprocessor(
obs_space.original_space["true_obs"])
self.action_embed_model = TorchFC(
Box(-10, 10, self.true_obs_preprocessor.shape), action_space,
action_embed_size, model_config, name + "_action_embed")
def __init__(
self,
obs_space,
action_space,
num_outputs,
model_config,
name,
**kwargs,
):
orig_space = getattr(obs_space, "original_space", obs_space)
assert isinstance(orig_space, Dict) and \
"action_mask" in orig_space.spaces and \
"observations" in orig_space.spaces
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name, **kwargs)
nn.Module.__init__(self)
self.internal_model = TorchFC(orig_space["observations"], action_space,
num_outputs, model_config,
name + "_internal")