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

License: Other

Python 100.00%

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rsl_rl's Issues

time out bootstrapping possible bug.

Hi, thank you for sharing this amazing code.

Recently, I've been looking into the detailed implementation of the code in relation to the paper "Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning."

From my understanding, based on the paper, the reward function is bootstrapped in the event of a time-out. I believe this bootstrapping should apply to the subsequent state, following the formula:
$r_{new} = r + v(s')$,
$s'$ represents the state resulting from the current step.
However, in the current implementation of the code, it appears to be executed as:
$r_{new} = r + v(s)$,
where $s$ is the state used for the current step.

Could you please clarify if my understanding aligns with the intended design?
I am curious to know whether this implementation choice was deliberate for specific reasons or if it might be an oversight.

Thank you for your time and assistance.

Where to find get_observations(self) -> torch.Tensor

Hi, I am wondering where I can find out how the get_observation function is fulfilled, it is a declaration of abstract method in vec_env.py. Thanks in advance!

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

RNN padding bug fix

I fixed a bug in the RNN padding and unpadding functions. Here is the PR: #12

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(),

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

Is rsl_rl v2.0.x supporting legged_gym?

Hi,

I am using legged_gym currently for legged robots training. I noticed that this package rsl_rl has been updated to 2.0 version while the required version for legged_gym is v1.0.2. I wonder if it is available to adopt this 2.0 version to my legged gym without potential problems, since I find myself in need of the newly-added tensorboard to view the trainning performance.

I am new to tensorboard and rl coding, so I'm afraid manually adding a tensorboard by myself to my original code may result in some problems. Thank you for your help : )

Wrong ETA for resumed trainings

Hello,

I noticed the ETA is wrong when the training is resumed from a previous run.

I guess you should replace locs['it'] (two occurrences) with locs['it'] - self.current_learning_iteration in the computation of the ETA (in runners/on_policy_runner.py, lines 209/210)

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))

Performance drop when resumed training with the empirical normalization

Hi there,

We noticed a performance drop when we resumed training with OnPolicyRunner which applied empirical normalization in our env.

There is a gap between the black line and blue one.
Additionally, we found the model performance cannot increase without the empirical normalization (The green and orange ones).

Many thanks.

mini_batch_generator, same indice in different epoch

The index generation operation for dataset is placed outside the loop, resulting that the data slices for each epoch remains the same. Is this a problem, or is this a less-than-ideal implementation?

The code is in /rsl_rl/storage/rollout_storage.py

How to uninstall rsl_rl and install v1.0.2?

I try to run pip3 uninstall rsl_rl, but it show

Found existing installation: rsl_rl 2.0.2
Can't uninstall 'rsl_rl'. No files were found to uninstall.

What should I do to remove rsl_rl and install v1.0.2

error

from rsl_rl.modules.models.rnn_state_encoder import build_rnn_state_encoder
ModuleNotFoundError: No module named 'rsl_rl.modules.models'

Benchmarking on the new implemented algorithms

Dear RSL,

Would it be possible to benchmark the newly implemented algorithms, especially the off-policy ones like SAC and TD3? I had some bad experiences tuning the batch size and updating numbers, as IsaacGym can generate too many samples per iteration. It would be nice if you could provide some insight.

Thanks,
Jin

Saving code diff fails if code contains non-utf characters like °

I had the degree sign (U+00B0) in a comment in my python code, and it caused store_code_state to fail. I think the fix here is as simple as changing open(diff_file_name, "x") to open(diff_file_name, "x", encoding="utf-8"). If I tested this and made a pull request, could someone review and merge?

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