prinshul
commented
10 months ago Exactly the same error with vmas/debug/diff_drive.
Closed prinshul closed 9 months ago
prinshul
commented
10 months ago Exactly the same error with vmas/debug/diff_drive.
matteobettini
commented
10 months ago You need to install torchrl main i think.
some stuff is fixed in the most recent versions
prinshul
commented
10 months ago You need to install torchrl main i think.
some stuff is fixed in the most recent versions
Current torchrl version 0.2.1 I think that's the recent one. Do you recommend installing it from the source (GitHub repo)?
matteobettini
commented
10 months ago matteobettini
commented
9 months ago Did this fix your issue?
prinshul
commented
9 months ago Sorry unfortunately the suggested fix was giving more errors. So I was unable to.
matteobettini
commented
9 months ago Are you using the latest version of both vmas and torchrl?
prinshul
commented
9 months ago Yes, I used the latest version at that time. Currently, I am not looking into it due to other engagements.
matteobettini
commented
9 months ago To show that it is possible to train those tasks (even though they are not included in the benchmarl pool of tasks) I have created a dedicated branch: https://github.com/facebookresearch/BenchMARL/tree/diff_drive
To train the diff_drive scenario, clone the repository, check out that branch and
Install requirements
pip install torchrl
pip install git+https://github.com/proroklab/VectorizedMultiAgentSimulator.git
pip install -e .Run with
python fine_tuned/vmas/vmas_run.pyThank you so much.
Error executing job with overrides: ['algorithm=mappo', 'task=vmas/debug/kinematic_bicycle'] Traceback (most recent call last): File "/hdd3/marl_new/new_bench_marl/BenchMARL/fine_tuned/vmas/vmas_run.py", line 25, in hydra_experiment experiment: Experiment = load_experiment_from_hydra(cfg, task_name=task_name) File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/hydra_config.py", line 37, in load_experiment_from_hydra return Experiment( File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/experiment/experiment.py", line 324, in init self._setup() File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/experiment/experiment.py", line 344, in _setup self._setup_algorithm() File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/experiment/experiment.py", line 421, in _setup_algorithm self.losses = { File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/experiment/experiment.py", line 422, in
group: self.algorithm.get_loss_and_updater(group)[0]
File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/algorithms/common.py", line 122, in get_loss_and_updater
policy_for_loss=self.get_policy_for_loss(group),
File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/algorithms/common.py", line 183, in get_policy_for_loss
group: self._get_policy_for_loss(
File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/algorithms/mappo.py", line 141, in _get_policy_for_loss
actor_module = model_config.get_model(
File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/models/common.py", line 250, in get_model
return self.associated_class()(
File "/hdd3/marl_new/new_bench_marl/BenchMARL/benchmarl/models/mlp.py", line 55, in init
self.mlp = MultiAgentMLP(
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torchrl/modules/models/multiagent.py", line 162, in init
[
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torchrl/modules/models/multiagent.py", line 163, in
MLP(
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torchrl/modules/models/models.py", line 214, in init
layers = self._make_net(device)
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torchrl/modules/models/models.py", line 225, in _make_net
create_on_device(
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torchrl/modules/models/utils.py", line 122, in create_on_device
return module_class(*args, device=device, kwargs)
File "/home/iitdpc/miniconda3/envs/mb_meta/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 96, in init
self.weight = Parameter(torch.empty((out_features, in_features), factory_kwargs))
RuntimeError: Trying to create tensor with negative dimension -2: [-2, 256]