81 lines
1.5 KiB
Python
81 lines
1.5 KiB
Python
import torch
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import numpy as np
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from pathlib import Path
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import matplotlib.pyplot as plt
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from multiprocessing import Pool
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# All of the following are required to load
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# a pickled model.
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from celeste_ai.celeste import Celeste
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from celeste_ai.network import DQN
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from celeste_ai.network import Transition
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def actual_reward(
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model_file: Path,
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target_point: tuple[int, int],
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out_filename: Path,
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*,
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device = torch.device("cpu")
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):
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if not model_file.is_file():
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raise Exception(f"Bad model file {model_file}")
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out_filename.parent.mkdir(exist_ok = True, parents = True)
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checkpoint = torch.load(
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model_file,
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map_location = device
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)
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memory = checkpoint["memory"]
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r = np.zeros((128, 128, 8), dtype=np.float32)
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for m in memory:
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x, y, x_target, y_target = list(m.state[0])
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action = m.action[0].item()
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x = int(x.item())
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y = int(y.item())
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x_target = int(x_target.item())
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y_target = int(y_target.item())
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# Only plot memory related to this point
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if (x_target, y_target) != target_point:
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continue
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if m.reward[0].item() == 1:
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r[y][x][action] += 1
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else:
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r[y][x][action] -= 1
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fig, axs = plt.subplots(2, 4, figsize = (20, 10))
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for a in range(len(axs.ravel())):
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ax = axs.ravel()[a]
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ax.set(
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adjustable = "box",
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aspect = "equal",
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title = Celeste.action_space[a]
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)
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plot = ax.pcolor(
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r[:,:,a],
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cmap = "seismic_r",
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vmin = -10,
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vmax = 10
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)
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# Draw target point on plot
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ax.plot(
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target_point[0],
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target_point[1],
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"k."
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)
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ax.invert_yaxis()
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fig.colorbar(plot)
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fig.savefig(out_filename)
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plt.close() |