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env = gym.make('MineRLObtainDiamond-v0')
env.seed(21)

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# Define the sequence of actions
script = ['forward'] * 5 + [''] * 40

env = gym.make('MineRLObtainDiamond-v0')
env = Recorder(env, './video', fps=60)
env.seed(21)
obs = env.reset()

for action in script:
    # Get the action space (dict of possible actions)

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script = []
script += [''] * 20 
script += ['forward'] * 5
script += ['attack'] * 61
script += ['camera:[-10,0]'] * 7  # Look up
script += ['attack'] * 240
script += ['jump']
script += ['forward'] * 10        # Jump forward
script += ['camera:[-10,0]'] * 2  # Look up
script += ['attack'] * 150

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class CNN(nn.Module):
    def __init__(self, input_shape, output_dim):
        super().__init__()
        n_input_channels = input_shape[0]
        self.cnn = nn.Sequential(
            nn.Conv2d(n_input_channels, 32, kernel_size=8, stride=4),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.BatchNorm2d(64),

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# Get data
minerl.data.download(directory='data', environment='MineRLTreechop-v0')
data = minerl.data.make("MineRLTreechop-v0", data_dir='data', num_workers=2)

# Model
model = CNN((3, 64, 64), 7).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
criterion = nn.CrossEntropyLoss()

# Training loop

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model = CNN((3, 64, 64), 7).cuda()
model.load_state_dict(torch.load('model.pth'))

env = gym.make('MineRLObtainDiamond-v0')
env1 = Recorder(env, './video', fps=60)
env = ActionShaping(env1)

action_list = np.arange(env.action_space.n)

obs = env.reset()

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obs = env_script.reset()
done = False

# 1. Get wood with the CNN
for i in tqdm(range(3000)):
    obs = torch.from_numpy(obs['pov'].transpose(2, 0, 1)[None].astype(np.float32) / 255).cuda()
    probabilities = torch.softmax(model(obs), dim=1)[0].detach().cpu().numpy()
    action = np.random.choice(action_list, p=probabilities)
    obs, reward, done, _ = env_script.step(action)

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import torch
import torch.nn.functional as F
from torch.nn import Linear, Sequential, BatchNorm1d, ReLU, Dropout
from torch_geometric.nn import GATConv
from torch_geometric.nn import global_add_pool


class GAT(torch.nn.Module):
    def __init__(self, dim_h):
        super(GAT, self).__init__()

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import numpy as np
import matplotlib.pyplot as plt

def softmax(x, temperature=1.0):
    e_x = np.exp(x / temperature)
    return e_x / e_x.sum(axis=0)

logits = np.array([1.5, -1.8, 0.9, -3.2])
temperatures = [1.0, 0.5, 0.1]

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