NVIDIA公司推出的GPU运行环境下的机器人仿真环境（NVIDIA Isaac Gym）的安装——强化学习的仿真训练环境 （续2）

紧接前文：

​​NVIDIA公司推出的GPU运行环境下的机器人仿真环境（NVIDIA Isaac Gym）的安装——强化学习的仿真训练环境​​

本文主要给出  NVIDIA Isaac Gym  在给出的pytorch下PPO算法下运行例子的运行命令例子：

下面就给出几个使用rlgpu文件下的reinforcement learning代码训练isaacgym环境的例子：

下面的例子使用的文件：/home/devil/isaacgym/python/rlgpu/train.py

rlgpu下面的train.py

使用help解释来查看NVIDIA给出的reinforcement leanring算法命令参数：

python train.py -h

RL Policy

optional arguments:
  -h, --help            show this help message and exit
  --sim_device SIM_DEVICE
                        Physics Device in PyTorch-like syntax
  --pipeline PIPELINE   Tensor API pipeline (cpu/gpu)
  --graphics_device_id GRAPHICS_DEVICE_ID
                        Graphics Device ID
  --flex                Use FleX for physics
  --physx               Use PhysX for physics
  --num_threads NUM_THREADS
                        Number of cores used by PhysX
  --subscenes SUBSCENES
                        Number of PhysX subscenes to simulate in parallel
  --slices SLICES       Number of client threads that process env slices
  --test                Run trained policy, no training
  --play                Run trained policy, the same as test, can be used only
                        by rl_games RL library
  --resume RESUME       Resume training or start testing from a checkpoint
  --checkpoint CHECKPOINT
                        Path to the saved weights, only for rl_games RL
                        library
  --headless            Force display off at all times
  --horovod             Use horovod for multi-gpu training, have effect only
                        with rl_games RL library
  --task TASK           Can be BallBalance, Cartpole, CartpoleYUp, Ant,
                        Humanoid, Anymal, FrankaCabinet, Quadcopter,
                        ShadowHand, Ingenuity
  --task_type TASK_TYPE
                        Choose Python or C++
  --rl_device RL_DEVICE
                        Choose CPU or GPU device for inferencing policy
                        network
  --logdir LOGDIR
  --experiment EXPERIMENT
                        Experiment name. If used with --metadata flag an
                        additional information about physics engine, sim
                        device, pipeline and domain randomization will be
                        added to the name
  --metadata            Requires --experiment flag, adds physics engine, sim
                        device, pipeline info and if domain randomization is
                        used to the experiment name provided by user
  --cfg_train CFG_TRAIN
  --cfg_env CFG_ENV
  --num_envs NUM_ENVS   Number of environments to create - override config
                        file
  --episode_length EPISODE_LENGTH
                        Episode length, by default is read from yaml config
  --seed SEED           Random seed
  --max_iterations MAX_ITERATIONS
                        Set a maximum number of training iterations
  --steps_num STEPS_NUM
                        Set number of simulation steps per 1 PPO iteration.
                        Supported only by rl_games. If not -1 overrides the
                        config settings.
  --minibatch_size MINIBATCH_SIZE
                        Set batch size for PPO optimization step. Supported
                        only by rl_games. If not -1 overrides the config
                        settings.
  --randomize           Apply physics domain randomization
  --torch_deterministic
                        Apply additional PyTorch settings for more
                        deterministic behaviour

运行命令例子：

1.  CPU上仿真，CPU上训练

在CPU上运行仿真环境，同时PPO深度强化学习算法在CPU上进行训练    #Simulation on CPU, training on CPU:

python  train.py --task=ShadowHand --headless --sim_device=cpu --rl_device=cpu --physx --num_threads=24

2.  CPU上仿真，GPU上训练

python  train.py --task=ShadowHand --headless --sim_device=cpu --rl_device=cuda:0 --physx --num_threads=24

3.  GPU上仿真，CPU上训练

python  train.py --task=ShadowHand --headless --sim_device=cuda:0 --rl_device=cpu  --physx --num_threads=24

4.  GPU上仿真，GPU上训练

其中，在0号显卡仿真，在1号显卡训练：

python  train.py --task=ShadowHand --headless --sim_device=cuda:0 --rl_device=cuda:1  --physx --num_threads=24

其中，在1号显卡仿真，在0号显卡训练：

python  train.py --task=ShadowHand --headless --sim_device=cuda:1  --rl_device=cuda:0  --physx --num_threads=24