BindsNET Breakout源码分析

from bindsnet.network import Network
from bindsnet.pipeline import EnvironmentPipeline
from bindsnet.encoding import bernoulli
from bindsnet.network.topology import Connection
from bindsnet.environment import GymEnvironment
from bindsnet.network.nodes import Input, IzhikevichNodes
from bindsnet.pipeline.action import select_softmax

# Build network.
network = Network(dt=1.0)

# Layers of neurons.
inpt = Input(n=80 * 80, shape=[1, 1, 1, 80, 80], traces=True)
middle = IzhikevichNodes(n=100, traces=True)
out = IzhikevichNodes(n=4, refrac=0, traces=True)

# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1)
middle_out = Connection(source=middle, target=out, wmin=0, wmax=1)

# Add all layers and connections to the network.
network.add_layer(inpt, name="Input Layer")
network.add_layer(middle, name="Hidden Layer")
network.add_layer(out, name="Output Layer")
network.add_connection(inpt_middle, source="Input Layer", target="Hidden Layer")
network.add_connection(middle_out, source="Hidden Layer", target="Output Layer")

# Load the Breakout environment.
environment = GymEnvironment("BreakoutDeterministic-v4")
environment.reset()

# Build pipeline from specified components.
pipeline = EnvironmentPipeline(
    network,
    environment,
    encoding=bernoulli,
    action_function=select_softmax,
    output="Output Layer",
    time=100,
    history_length=1,
    delta=1,
    plot_interval=1,
    render_interval=1,
)

# Run environment simulation for 100 episodes.
for i in range(100):
    total_reward = 0
    pipeline.reset_state_variables()
    is_done = False
    while not is_done:
        result = pipeline.env_step()
        pipeline.step(result)

        reward = result[1]
        total_reward += reward

        is_done = result[2]
    print(f"Episode {i} total reward:{total_reward}")