Gobigger Explore For Python

Related tags

Miscellaneous Gobigger-Explore
Overview

Gobigger-Explore

🔮 GoBigger Challenge 2021 Baseline

en/中文

🤖 Introduction

This is the baseline of GoBigger Multi-Agent Decision Intelligence Challenge in 2021. The baseline is based on OpenDILab and aims to provide a simple entry-level method. Participants can build agents by extending the baseline method provided. In addition, Opendilab's modular structure allows participants to easily get started, and it provides a wealth of reinforcement learning algorithms for participants to use. This baseline is a good starting point, especially for entry-level researchers who are familiar with multi-agent decision AI problems.

🚀 Release Version

The current version is the latest version v-0.2.0.

  1. What needs to be optimized in the future
    • Variable length features lead to potential GPU memory explosion risk.
    • Better reward function design.
  2. Version-0.2.0
    • Fix the ckpt bug to improve the accuracy of the evaluator.
    • Fix replay_buffer bug
    • Brand new feature engineering to improve convergence speed.
    • Replay_buffer stores variable-length features to improve data utilization and training speed.
  3. Version-0.1.0

👇 Getting Started

  1. System environment

    • Core 16
    • GPU A100(40G)
    • Memory 50G

  2. Baseline Config

    • The default config is the config used in this experiment. Participants can modify it according to the system environment.
    • The size of replay_buffer_size needs to be set according to the size of RAM.
    • The size of batch_size needs to be set according to the size of the GPU memory.

  3. Install the necessary packege

    # Install DI-engine
    git clone https://github.com/opendilab/DI-engine.git
    cd YOUR_PATH/DI-engine/
    pip install -e . --user

    # Install Env Gobigger
    git clone https://github.com/opendilab/GoBigger.git
    cd YOUR_PATH/GoBigger/
    pip install -e . --user
  1. Start training
    # Download baseline
    git clone https://github.com/opendilab/Gobigger-Explore.git
    cd my_submission/entry/
    python gobigger_vsbot_baseline_main.py
  1. Evaluator and Save game videos
    cd my_submission/entry/
    python gobigger_vsbot_baseline_eval.py --ckpt YOUR_CKPT_PATH

🎯 Result

We released training log information, checkpoints, and evaluation videos. Below is the download link,

  • Baidu Netdisk Link
    • Extraction code: u4i6
  • Google Drive Link

😍 Resources

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Comments
  • typeerros when i run 'python gobigger_vsbot_baseline_main.py'

    typeerros when i run 'python gobigger_vsbot_baseline_main.py' 

    pygame 2.1.0 (SDL 2.0.16, Python 3.7.9)
Hello from the pygame community. https://www.pygame.org/contribute.html
INFO:learner_logger:[RANK0]: DI-engine DRL Policy
GoBiggerHybridActionSimple(
  (activation): ReLU()
  (encoder): StructedGcnConvSimple(
    (scalar_encoder): Sequential(
      (0): Linear(in_features=5, out_features=32, bias=True)
      (1): ReLU()
      (2): Linear(in_features=32, out_features=128, bias=True)
      (3): ReLU()
    )
    (food_encoder): Sequential(
      (0): Conv2d(2, 32, kernel_size=(5, 5), stride=(4, 4), padding=(2, 2))
      (1): ReLU()
      (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (3): ReLU()
      (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (5): ReLU()
      (6): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (7): ReLU()
      (8): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (9): ReLU()
    )
    (food_relation_encoder): Sequential(
      (0): Linear(in_features=150, out_features=64, bias=True)
      (1): ReLU()
      (2): Linear(in_features=64, out_features=128, bias=True)
      (3): ReLU()
    )
    (thorn_relation_encoder): Sequential(
      (0): Linear(in_features=12, out_features=32, bias=True)
      (1): ReLU()
      (2): Linear(in_features=32, out_features=128, bias=True)
      (3): ReLU()
    )
    (clone_encoder): Sequential(
      (0): Linear(in_features=17, out_features=32, bias=True)
      (1): ReLU()
      (2): Linear(in_features=32, out_features=128, bias=True)
      (3): ReLU()
    )
    (clone_relation_encoder): Sequential(
      (0): Linear(in_features=12, out_features=32, bias=True)
      (1): ReLU()
      (2): Linear(in_features=32, out_features=128, bias=True)
      (3): ReLU()
    )
    (gcn_1): RelationGCN(
      (act): ReLU()
      (thorn_relation_layers): Sequential(
        (0): Linear(in_features=256, out_features=128, bias=True)
        (1): ReLU()
      )
      (clone_relation_layers): Sequential(
        (0): Linear(in_features=384, out_features=128, bias=True)
        (1): ReLU()
      )
      (agg_relation_layers): Sequential(
        (0): Linear(in_features=512, out_features=128, bias=True)
        (1): ReLU()
      )
    )
    (gcn_2): RelationGCN(
      (act): ReLU()
      (thorn_relation_layers): Sequential(
        (0): Linear(in_features=256, out_features=128, bias=True)
        (1): ReLU()
      )
      (clone_relation_layers): Sequential(
        (0): Linear(in_features=384, out_features=128, bias=True)
        (1): ReLU()
      )
      (agg_relation_layers): Sequential(
        (0): Linear(in_features=512, out_features=128, bias=True)
        (1): ReLU()
      )
    )
    (agg_encoder): Sequential(
      (0): Linear(in_features=384, out_features=128, bias=True)
      (1): ReLU()
      (2): Linear(in_features=128, out_features=64, bias=True)
      (3): ReLU()
    )
  )
  (action_type_head): DiscreteHead(
    (Q): Sequential(
      (0): Sequential(
        (0): Linear(in_features=32, out_features=32, bias=True)
        (1): ReLU()
        (2): Linear(in_features=32, out_features=32, bias=True)
        (3): ReLU()
      )
      (1): Sequential(
        (0): Linear(in_features=32, out_features=16, bias=True)
      )
    )
  )
)
Traceback (most recent call last):
  File "/home/keliangjun666/hjb/Gobigger-Explore/my_submission/entry/gobigger_vsbot_baseline_simple_main.py", line 112, in <module>
    main(main_config)
  File "/home/keliangjun666/hjb/Gobigger-Explore/my_submission/entry/gobigger_vsbot_baseline_simple_main.py", line 99, in main
    learner.save_checkpoint, learner.train_iter, collector.envstep
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/worker/collector/battle_interaction_serial_evaluator.py", line 206, in eval
    obs = to_tensor(obs, dtype=torch.float32)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 121, in to_tensor
    new_data.append(to_tensor(t, dtype, ignore_keys, transform_scalar))
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 121, in to_tensor
    new_data.append(to_tensor(t, dtype, ignore_keys, transform_scalar))
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 121, in to_tensor
    new_data.append(to_tensor(t, dtype, ignore_keys, transform_scalar))
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 109, in to_tensor
    new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 121, in to_tensor
    new_data.append(to_tensor(t, dtype, ignore_keys, transform_scalar))
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 115, in to_tensor
    return transform(item)
  File "/home/keliangjun666/miniconda3/envs/big/lib/python3.7/site-packages/ding/torch_utils/data_helper.py", line 101, in transform
    return torch.tensor(d, dtype=dtype)
TypeError: must be real number, not str

    how i can slove this error

    opened by notomatoes 1
  • when I use Multi-GPUs to train my model，RuntimeError: Caught RuntimeError in replica 0 on device 0

    when I use Multi-GPUs to train my model，RuntimeError: Caught RuntimeError in replica 0 on device 0

    When I test the DP mode ,the error happened image

    Refer to the https://di-engine-docs.readthedocs.io/zh_CN/latest/best_practice/multi_gpu_example.html， I changed the file： （1）gobigger_no_spatial_config.py image （2）gobigger_vsbot_baseline_simple_main.py image

    opened by temporaryforijcai 1
  • How to speed up?

    How to speed up?

    Hi, nice baseline repo! Your log of v0.3 shows that it takes more than 3 days to train this model. I have tried to use ddp to speed up the training process but still not satisfied. Do you have any suggestion for speeding the training process up in a multi-gpu and high-memory machine? Any useful params to share?

    opened by applenob 1
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