def get_topology(pop_name, gid: int, cfg: Config):
"""
Create a uniformly and randomly sampled genome of fixed topology:
Sigmoid with bias 1.5 --> Actuation default of 95,3%
(key=0, bias=1.5) (key=1, bias=?)
____ / /
/ /
GRU /
| _____/
| /
(key=-1)
"""
# Create an initial dummy genome with fixed configuration
genome = Genome(
key=gid,
num_outputs=cfg.genome.num_outputs,
bot_config=cfg.bot,
)
# Setup the parameter-ranges
conn_range = cfg.genome.weight_max_value - cfg.genome.weight_min_value
bias_range = cfg.genome.bias_max_value - cfg.genome.bias_min_value
rnn_range = cfg.genome.rnn_max_value - cfg.genome.rnn_min_value
# Create the output nodes
genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0
genome.nodes[0].bias = 1.5 # Drive with 0.953 actuation by default
genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1
if pop_name in [P_BIASED]:
genome.nodes[1].bias = normal(
1.5,
.1) # Initially normally distributed around bias of other output
else:
genome.nodes[1].bias = random(
) * bias_range + cfg.genome.bias_min_value # Uniformly sampled bias
# Setup the recurrent unit
if pop_name in [P_GRU_NR]:
genome.nodes[2] = GruNoResetNodeGene(key=2,
cfg=cfg.genome,
input_keys=[-1],
input_keys_full=[-1]) # Hidden
genome.nodes[2].bias_h = rand_arr(
(2, )) * bias_range + cfg.genome.bias_min_value
genome.nodes[2].weight_xh_full = rand_arr(
(2, 1)) * rnn_range + cfg.genome.weight_min_value
genome.nodes[2].weight_hh = rand_arr(
(2, 1)) * rnn_range + cfg.genome.weight_min_value
else:
genome.nodes[2] = GruNodeGene(key=2,
cfg=cfg.genome,
input_keys=[-1],
input_keys_full=[-1]) # Hidden node
genome.nodes[2].bias_h = rand_arr(
(3, )) * bias_range + cfg.genome.bias_min_value
genome.nodes[2].weight_xh_full = rand_arr(
(3, 1)) * rnn_range + cfg.genome.weight_min_value
genome.nodes[2].weight_hh = rand_arr(
(3, 1)) * rnn_range + cfg.genome.weight_min_value
genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node
# Create the connections
genome.connections = dict()
# input2gru - Uniformly sampled on the positive spectrum
key = (-1, 2)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
if pop_name in [P_BIASED]:
genome.connections[
key].weight = 6 # Maximize connection, GRU can always lower values flowing through
else:
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
genome.connections[key].enabled = True
# gru2output - Uniformly sampled on the positive spectrum
key = (2, 1)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
if pop_name in [P_BIASED]:
genome.connections[key].weight = abs(
genome.connections[key].weight) # Always positive!
genome.connections[key].enabled = True
# input2output - Uniformly sampled
key = (-1, 1)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
if pop_name in [P_BIASED]:
genome.connections[key].weight = -abs(
genome.connections[key].weight) # Always negative!
genome.connections[key].enabled = True
# Enforce the topology constraints
enforce_topology(pop_name=pop_name, genome=genome)
genome.update_rnn_nodes(config=cfg.genome)
return genome
def get_topology3333(gid: int, cfg: Config):
"""
Create a uniformly and randomly sampled genome of fixed topology:
Sigmoid with bias 1.5 --> Actuation default of 95,3%
(key=0, bias=1.5) (key=1, bias=?)
____ / /
/ /
GRU-NR /
| _____/
| /
(key=-1)
"""
# Create an initial dummy genome with fixed configuration
genome = Genome(
key=gid,
num_outputs=cfg.genome.num_outputs,
bot_config=cfg.bot,
)
# Setup the parameter-ranges
conn_range = cfg.genome.weight_max_value - cfg.genome.weight_min_value
bias_range = cfg.genome.bias_max_value - cfg.genome.bias_min_value
rnn_range = cfg.genome.rnn_max_value - cfg.genome.rnn_min_value
# Create the nodes
genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0
genome.nodes[0].bias = 1.5 # Drive with 0.953 actuation by default
genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1
genome.nodes[1].bias = random(
) * bias_range + cfg.genome.bias_min_value # Uniformly sampled bias
genome.nodes[2] = GruNoResetNodeGene(key=2,
cfg=cfg.genome,
input_keys=[-1],
input_keys_full=[-1]) # Hidden node
genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node
# Uniformly sample the genome's GRU-component
genome.nodes[2].bias_h = rand_arr(
(2, )) * bias_range + cfg.genome.bias_min_value
genome.nodes[2].weight_xh_full = rand_arr(
(2, 1)) * rnn_range + cfg.genome.weight_min_value
genome.nodes[2].weight_hh = rand_arr(
(2, 1)) * rnn_range + cfg.genome.weight_min_value
# Create the connections
genome.connections = dict()
# input2gru
key = (-1, 2)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
genome.connections[key].enabled = True
# gru2output - Uniformly sampled
key = (2, 1)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
genome.connections[key].enabled = True
# input2output - Uniformly sampled
key = (-1, 1)
genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome)
genome.connections[
key].weight = random() * conn_range + cfg.genome.weight_min_value
genome.connections[key].enabled = True
genome.update_rnn_nodes(config=cfg.genome)
return genome
