Comments (6)
1e8个step足够了, n_training_thread 置为1,训练再试试
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我的训练参数如下
python train_jsbsim.py --env-name SingleCombat --algorithm-name ppo --scenario-name 1v1/NoWeapon/Selfplay --experiment-name v1 --seed 1 --n-training-threads 64 --n-rollout-threads 32 --log-interval 1 --save-interval 1 --cuda --use-selfplay --selfplay-algorithm "fsp" --n-choose-opponents 1 --eval-interval 1 --eval-episodes 1 --num-mini-batch 5 --buffer-size 3000 --num-env-steps 1e9 --lr 3e-4 --gamma 0.99 --ppo-epoch 4 --clip-params 0.2 --max-grad-norm 2 --entropy-coef 1e-3 --hidden-size "128 128" --act-hidden-size "128 128" --recurrent-hidden-size 128 --recurrent-hidden-layers 1 --data-chunk-length 8
在我分别训练了1e8次step和2e8次steps再渲染后,好像并没有得到预期的效果,再tacview中只有30秒的时间,渲染结果output如下:
请问是模型的问题嘛,还是我训练时间不够,我看着1e8次的训练,奖励曲线也已经收敛到70多分啦,再接着训练奖励曲线也没有变化了
from closeaircombat.
你好,n_training_thread 置为1训练后,今天渲染仍然是这个问题
from closeaircombat.
你好,关于这个环境的安装问题想请教您一下,请问您方便吗,可以的话感激不尽
from closeaircombat.
请问您环境安装成功了吗?想请教一下最后一步- Initialize submodules(JSBSim-Team/jsbsim): git submodule init; git submodule update
是怎么进行的呢
from closeaircombat.
重新验证了一下,根据intro介绍的流程,1v1/NoWeapon/Selfplay能训练出效果。
from closeaircombat.
Related Issues (20)
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