name='Input Layer'
)
action_selection = pnl.TransferMechanism(
size=3,
function=pnl.SoftMax(
output=pnl.PROB,
gain=1.0
),
name='Action Selection'
)
p = pnl.Process(
default_variable=[0, 0, 0],
pathway=[input_layer, action_selection],
learning=pnl.LearningProjection(learning_function=pnl.Reinforcement(learning_rate=0.05)),
target=0
)
print('reward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
print('target_mechanism weights: \n', action_selection.output_state.efferents[0].matrix)
actions = ['left', 'middle', 'right']
reward_values = [10, 0, 0]
first_reward = 0
# Must initialize reward (won't be used, but needed for declaration of lambda function)
action_selection.output_state.value = [0, 0, 1]
# Get reward value for selected action)
import functools
import numpy as np
import psyneulink as pnl
input_layer = pnl.TransferMechanism(default_variable=[0, 0, 0],
name='Input Layer')
action_selection = pnl.TransferMechanism(default_variable=[0, 0, 0],
function=pnl.SoftMax(output=pnl.PROB,
gain=1.0),
name='Action Selection')
p = pnl.Process(default_variable=[0, 0, 0],
pathway=[input_layer, action_selection],
learning=pnl.LearningProjection(
learning_function=pnl.Reinforcement(learning_rate=0.05)),
target=0)
print('reward prediction weights: \n',
action_selection.input_state.path_afferents[0].matrix)
print('target_mechanism weights: \n',
action_selection.output_state.efferents[0].matrix)
actions = ['left', 'middle', 'right']
reward_values = [10, 10, 10]
first_reward = 0
# Must initialize reward (won't be used, but needed for declaration of lambda function)
action_selection.output_state.value = [0, 0, 1]
# Get reward value for selected action)