Fast and simple implementation of RL algorithms, designed to run fully on GPU.

Related tags

Deep Learning rsl_rl
Overview

RSL RL

Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's Isaac GYM.

Only PPO is implemented for now. More algorithms will be added later. Contributions are welcome.

Setup

git clone https://github.com/leggedrobotics/rsl_rl
cd rsl_rl
pip install -e .

Maintainer: Nikita Rudin
Affiliation: Robotic Systems Lab, ETH Zurich & NVIDIA Contact: [email protected]

Comments
  • Citation

    Citation

    Hi @nikitardn , if this repo was used for academic work, which citation should we use? The same as in legged_gym? (Should maybe also be added to the readme) Thanks

    opened by EricVoll 1
  • There is an error with installation

    There is an error with installation

    In the setup.py, the third and ninth lines have repeated statements.This will cause an error during installation. You may need to delete line 3 in setup.py. packages=find_packages(),

    opened by cwjwudi 0
  • fix iteration increments

    fix iteration increments

    In on_policy_learning.py, current_learning_iteration is only being incremented at the end of the learning run, so all intermediate saves have the incorrect iteration. This is fixed, so if you resume with an intermediate checkpoint, it starts counting from the correct place (mainly just a nice-to-have for the tensorboard visualizations).

    opened by sheim 0
  • action noise parameterization

    action noise parameterization

    https://github.com/leggedrobotics/rsl_rl/blob/master/rsl_rl/modules/actor_critic.py#L121 The action noise had better use positive parameterization, e.g. self.distribution = Normal(mean, torch.ones_like(mean) + nn.functional.elu(self.std - 1))

    opened by twu-mrc 0
  • Error while training with legged_gym

    Error while training with legged_gym

    I have installed the legged_gym and am trying to run the default task of the ANYmal C robot on flat terrain.

    However, after a few seconds of simulation (The simulator screen appears and the robots show some actions being applied to them), the following error is encountered. I am not sure how to solve this, however, it seems that this is caused by the rsl_rl package rather than the legged_gym package.

    I am attaching the output below. Kindly advise on how to resolve this.

    Importing module 'gym_37' (/home/arl/Documents/isaacgym/python/isaacgym/_bindings/linux-x86_64/gym_37.so)
    Setting GYM_USD_PLUG_INFO_PATH to /home/arl/Documents/isaacgym/python/isaacgym/_bindings/linux-x86_64/usd/plugInfo.json
    PyTorch version 1.8.1
    Device count 2
    /home/arl/Documents/isaacgym/python/isaacgym/_bindings/src/gymtorch
    Using /home/arl/.cache/torch_extensions as PyTorch extensions root...
    Emitting ninja build file /home/arl/.cache/torch_extensions/gymtorch/build.ninja...
    Building extension module gymtorch...
    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
    ninja: no work to do.
    Loading extension module gymtorch...
    Setting seed: 1
    Not connected to PVD
    +++ Using GPU PhysX
    Physics Engine: PhysX
    Physics Device: cuda:0
    GPU Pipeline: enabled
    Actor MLP: Sequential(
      (0): Linear(in_features=48, out_features=128, bias=True)
      (1): ELU(alpha=1.0)
      (2): Linear(in_features=128, out_features=64, bias=True)
      (3): ELU(alpha=1.0)
      (4): Linear(in_features=64, out_features=32, bias=True)
      (5): ELU(alpha=1.0)
      (6): Linear(in_features=32, out_features=12, bias=True)
    )
    Critic MLP: Sequential(
      (0): Linear(in_features=48, out_features=128, bias=True)
      (1): ELU(alpha=1.0)
      (2): Linear(in_features=128, out_features=64, bias=True)
      (3): ELU(alpha=1.0)
      (4): Linear(in_features=64, out_features=32, bias=True)
      (5): ELU(alpha=1.0)
      (6): Linear(in_features=32, out_features=1, bias=True)
    )
    /home/arl/miniconda3/envs/rlgpu/lib/python3.7/site-packages/torch/nn/modules/module.py:889: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at  /opt/conda/conda-bld/pytorch_1616554800319/work/aten/src/ATen/native/cudnn/RNN.cpp:915.)
      result = self.forward(*input, **kwargs)
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    Traceback (most recent call last):
      File "legged_gym/scripts/train.py", line 47, in <module>
        train(args)
      File "legged_gym/scripts/train.py", line 43, in train
        ppo_runner.learn(num_learning_iterations=train_cfg.runner.max_iterations, init_at_random_ep_len=True)
      File "/home/arl/Documents/rsl_rl/rsl_rl/runners/on_policy_runner.py", line 132, in learn
        mean_value_loss, mean_surrogate_loss = self.alg.update()
      File "/home/arl/Documents/rsl_rl/rsl_rl/algorithms/ppo.py", line 131, in update
        self.actor_critic.act(obs_batch, masks=masks_batch, hidden_states=hid_states_batch[0])
      File "/home/arl/Documents/rsl_rl/rsl_rl/modules/actor_critic.py", line 124, in act
        self.update_distribution(observations)
      File "/home/arl/Documents/rsl_rl/rsl_rl/modules/actor_critic.py", line 121, in update_distribution
        self.distribution = Normal(mean, mean*0. + self.std)
      File "/home/arl/miniconda3/envs/rlgpu/lib/python3.7/site-packages/torch/distributions/normal.py", line 50, in __init__
        super(Normal, self).__init__(batch_shape, validate_args=validate_args)
      File "/home/arl/miniconda3/envs/rlgpu/lib/python3.7/site-packages/torch/distributions/distribution.py", line 52, in __init__
        if not constraint.check(getattr(self, param)).all():
    RuntimeError: CUDA error: device-side assert triggered
    
    opened by mihirk284 0
Owner
Robotic Systems Lab - Legged Robotics at ETH Zürich
The Robotic Systems Lab investigates the development of machines and their intelligence to operate in rough and challenging environments.
Robotic Systems Lab - Legged Robotics at ETH Zürich
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