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MiniGrid-DistShift1-v0

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  1. events.out.tfevents.1693811560.rl-lm-v100-ubuntu +3 -0
  2. log.csv +0 -0
  3. log.txt +289 -0
  4. status.pt +3 -0
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log.csv ADDED
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+ /home/qjx/Data_collector/rl-starter-files/scripts/train.py --algo ppo --env MiniGrid-DistShift1-v0 --model MiniGrid-DistShift1-v0 --save-interval 40 --frames 10000 --discount 0.9 --text
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+
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+ Namespace(algo='ppo', batch_size=256, clip_eps=0.2, discount=0.9, entropy_coef=0.01, env='MiniGrid-DistShift1-v0', epochs=4, frames=10000, frames_per_proc=128, gae_lambda=0.95, log_interval=1, lr=0.001, max_grad_norm=0.5, mem=False, model='MiniGrid-DistShift1-v0', optim_alpha=0.99, optim_eps=1e-08, procs=16, recurrence=1, save_interval=40, seed=1, text=True, value_loss_coef=0.5)
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+
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+ Device: cuda
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+
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+ Environments loaded
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+
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+ Training status loaded
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+
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+ Observations preprocessor loaded
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+ Model loaded
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+
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+ ACModel(
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+ (image_conv): Sequential(
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+ (0): Conv2d(3, 16, kernel_size=(2, 2), stride=(1, 1))
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+ (1): ReLU()
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+ (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
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+ (3): Conv2d(16, 32, kernel_size=(2, 2), stride=(1, 1))
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+ (4): ReLU()
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+ (5): Conv2d(32, 64, kernel_size=(2, 2), stride=(1, 1))
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+ (6): ReLU()
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+ )
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+ (word_embedding): Embedding(100, 32)
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+ (text_rnn): GRU(32, 128, batch_first=True)
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+ (actor): Sequential(
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+ (0): Linear(in_features=256, out_features=64, bias=True)
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+ (1): Tanh()
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+ (2): Linear(in_features=64, out_features=7, bias=True)
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+ )
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+ (critic): Sequential(
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+ (0): Linear(in_features=256, out_features=64, bias=True)
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+ (1): Tanh()
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+ (2): Linear(in_features=64, out_features=1, bias=True)
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+ )
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+ )
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+
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+ Optimizer loaded
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+
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+ Status saved
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status.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b26febc5a9d44bf814684059ed25638c83ad09922c5f41a4c855794a03ccc286
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+ size 1330943