| /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 | |
| 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) | |
| Device: cuda | |
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| ACModel( | |
| (image_conv): Sequential( | |
| (0): Conv2d(3, 16, kernel_size=(2, 2), stride=(1, 1)) | |
| (1): ReLU() | |
| (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False) | |
| (3): Conv2d(16, 32, kernel_size=(2, 2), stride=(1, 1)) | |
| (4): ReLU() | |
| (5): Conv2d(32, 64, kernel_size=(2, 2), stride=(1, 1)) | |
| (6): ReLU() | |
| ) | |
| (word_embedding): Embedding(100, 32) | |
| (text_rnn): GRU(32, 128, batch_first=True) | |
| (actor): Sequential( | |
| (0): Linear(in_features=256, out_features=64, bias=True) | |
| (1): Tanh() | |
| (2): Linear(in_features=64, out_features=7, bias=True) | |
| ) | |
| (critic): Sequential( | |
| (0): Linear(in_features=256, out_features=64, bias=True) | |
| (1): Tanh() | |
| (2): Linear(in_features=64, out_features=1, bias=True) | |
| ) | |
| ) | |
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