add jumanji env;
add repeat times for rl_env
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wls2002
193
tensorneat/algorithm/neat/gene/node/min_max_node.py
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193
tensorneat/algorithm/neat/gene/node/min_max_node.py
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from typing import Tuple
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import jax, jax.numpy as jnp
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from utils import Act, Agg, act_func, agg_func, mutate_int, mutate_float
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from . import BaseNodeGene
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class MinMaxNode(BaseNodeGene):
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"""
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Node with normalization before activation.
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"""
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# alpha and beta is used for normalization, just like BatchNorm
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# norm: z = act(agg(inputs) + bias)
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# z = (z - min) / (max - min) * (max_out - min_out) + min_out
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custom_attrs = ["bias", "aggregation", "activation", "min", "max"]
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eps = 1e-6
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def __init__(
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self,
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bias_init_mean: float = 0.0,
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bias_init_std: float = 1.0,
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bias_mutate_power: float = 0.5,
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bias_mutate_rate: float = 0.7,
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bias_replace_rate: float = 0.1,
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aggregation_default: callable = Agg.sum,
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aggregation_options: Tuple = (Agg.sum,),
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aggregation_replace_rate: float = 0.1,
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activation_default: callable = Act.sigmoid,
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activation_options: Tuple = (Act.sigmoid,),
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activation_replace_rate: float = 0.1,
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output_range: Tuple[float, float] = (-1, 1),
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update_hidden_node: bool = False,
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):
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super().__init__()
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self.bias_init_mean = bias_init_mean
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self.bias_init_std = bias_init_std
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self.bias_mutate_power = bias_mutate_power
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self.bias_mutate_rate = bias_mutate_rate
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self.bias_replace_rate = bias_replace_rate
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self.aggregation_default = aggregation_options.index(aggregation_default)
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self.aggregation_options = aggregation_options
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self.aggregation_indices = jnp.arange(len(aggregation_options))
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self.aggregation_replace_rate = aggregation_replace_rate
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self.activation_default = activation_options.index(activation_default)
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self.activation_options = activation_options
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self.activation_indices = jnp.arange(len(activation_options))
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self.activation_replace_rate = activation_replace_rate
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self.output_range = output_range
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assert (
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len(self.output_range) == 2 and self.output_range[0] < self.output_range[1]
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)
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self.update_hidden_node = update_hidden_node
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def new_identity_attrs(self, state):
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return jnp.array(
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[0, self.aggregation_default, -1, 0, 1]
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) # activation=-1 means Act.identity; min=0, max=1 will do not influence
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def new_random_attrs(self, state, randkey):
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k1, k2, k3, k4, k5 = jax.random.split(randkey, num=5)
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bias = jax.random.normal(k1, ()) * self.bias_init_std + self.bias_init_mean
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agg = jax.random.randint(k2, (), 0, len(self.aggregation_options))
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act = jax.random.randint(k3, (), 0, len(self.activation_options))
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return jnp.array([bias, agg, act, 0, 1])
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def mutate(self, state, randkey, attrs):
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k1, k2, k3, k4, k5 = jax.random.split(randkey, num=5)
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bias, act, agg, min_, max_ = attrs
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bias = mutate_float(
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k1,
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bias,
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self.bias_init_mean,
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self.bias_init_std,
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self.bias_mutate_power,
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self.bias_mutate_rate,
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self.bias_replace_rate,
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)
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agg = mutate_int(
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k2, agg, self.aggregation_indices, self.aggregation_replace_rate
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)
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act = mutate_int(k3, act, self.activation_indices, self.activation_replace_rate)
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return jnp.array([bias, agg, act, min_, max_])
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def distance(self, state, attrs1, attrs2):
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bias1, agg1, act1, min1, max1 = attrs1
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bias2, agg2, act2, min1, max1 = attrs2
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return (
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jnp.abs(bias1 - bias2) # bias
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+ (agg1 != agg2) # aggregation
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+ (act1 != act2) # activation
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)
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def forward(self, state, attrs, inputs, is_output_node=False):
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"""
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post_act = (agg(inputs) + bias - mean) / std * alpha + beta
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"""
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bias, agg, act, min_, max_ = attrs
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z = agg_func(agg, inputs, self.aggregation_options)
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z = bias + z
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# the last output node should not be activated
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z = jax.lax.cond(
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is_output_node, lambda: z, lambda: act_func(act, z, self.activation_options)
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)
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if self.update_hidden_node:
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z = (z - min_) / (max_ - min_) # transform to 01
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z = (
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z * (self.output_range[1] - self.output_range[0]) + self.output_range[0]
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) # transform to output_range
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return z
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def input_transform(self, state, attrs, inputs):
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"""
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make transform in the input node.
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the normalization also need be done in the first node.
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"""
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bias, agg, act, min_, max_ = attrs
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inputs = (inputs - min_) / (max_ - min_) # transform to 01
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inputs = (
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inputs * (self.output_range[1] - self.output_range[0])
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+ self.output_range[0]
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)
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return inputs
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def update_by_batch(self, state, attrs, batch_inputs, is_output_node=False):
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bias, agg, act, min_, max_ = attrs
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batch_z = jax.vmap(agg_func, in_axes=(None, 0, None))(
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agg, batch_inputs, self.aggregation_options
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)
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batch_z = bias + batch_z
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batch_z = jax.lax.cond(
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is_output_node,
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lambda: batch_z,
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lambda: jax.vmap(act_func, in_axes=(None, 0, None))(
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act, batch_z, self.activation_options
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),
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)
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if self.update_hidden_node:
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# calculate min, max
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min_ = jnp.min(jnp.where(jnp.isnan(batch_z), jnp.inf, batch_z))
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max_ = jnp.max(jnp.where(jnp.isnan(batch_z), -jnp.inf, batch_z))
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batch_z = (batch_z - min_) / (max_ - min_) # transform to 01
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batch_z = (
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batch_z * (self.output_range[1] - self.output_range[0])
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+ self.output_range[0]
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)
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# update mean and std to the attrs
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attrs = attrs.at[3].set(min_)
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attrs = attrs.at[4].set(max_)
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return batch_z, attrs
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def update_input_transform(self, state, attrs, batch_inputs):
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"""
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update the attrs for transformation in the input node.
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default: do nothing
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"""
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bias, agg, act, min_, max_ = attrs
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# calculate min, max
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min_ = jnp.min(jnp.where(jnp.isnan(batch_inputs), jnp.inf, batch_inputs))
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max_ = jnp.max(jnp.where(jnp.isnan(batch_inputs), -jnp.inf, batch_inputs))
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batch_inputs = (batch_inputs - min_) / (max_ - min_) # transform to 01
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batch_inputs = (
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batch_inputs * (self.output_range[1] - self.output_range[0])
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+ self.output_range[0]
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)
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# update mean and std to the attrs
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attrs = attrs.at[3].set(min_)
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attrs = attrs.at[4].set(max_)
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return batch_inputs, attrs
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