[Feature] Vectorized lidar

[matteobettini]

continuation of #122

What we vectorize

Let me recap the levels of looping:

1. there are multiple agents

2. each agent can have multiple lidars

3. each lidar sees multtiple entities

4. at multiple angles

5. in multiple environments

   And here is what we vectorize:

6. I wouldn't vectorize this as of now as not all agents have lidars

7. i also would not vectorize this as the user might want to read out different lidars at different times (although I would consider it in the future)

8. in this PR

9. in this PR

10. vmas does this already

Benchmarking

pollock scenario

use_vmas_env(
        scenario_name="pollock",
        render=False,
        save_render=False,
        random_action=True,
        continuous_actions=False,
        n_agents=15,
        n_lines=15,
        n_boxes=15,
        lidar=True,
        n_steps=100,
        num_envs=32,
 )

before PR:

M1 cpu: 134.55210709571838s CUDA: 571.7651047706604s

after PR:

M1 cpu: 22.2061710357666s (0.16x the previous time) CUDA: 91.27248549461365s (0.15 x the previous time)

benefit will increase in the number of steps and envs

🚀

[codecov[bot]]

Codecov Report

Attention: Patch coverage is 99.46524% with 1 line in your changes missing coverage. Please review.

Project coverage is 88.22%. Comparing base (a9d545f) to head (418ad04).

Files	Patch %	Lines
vmas/simulator/core.py	99.37%	1 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #124 +/- ## ========================================== + Coverage 87.47% 88.22% +0.75% ========================================== Files 82 83 +1 Lines 9507 9688 +181 ========================================== + Hits 8316 8547 +231 + Misses 1191 1141 -50 ```

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[matteobettini]

@Zartris if you wanna review / try it out this shoulf be mega fasttttt

[Zartris]

Oh that was quick! Ill take a look today

[matteobettini]

pollock scenario

use_vmas_env(
        scenario_name="pollock",
        render=False,
        save_render=False,
        random_action=True,
        continuous_actions=False,
        n_agents=15,
        n_lines=15,
        n_boxes=15,
        lidar=True,
        n_steps=100,
        num_envs=32,
 )

before PR:

• M1 cpu: 134.55210709571838s
• CUDA: 571.7651047706604s

after PR:

• M1 cpu: 22.2061710357666s (0.16x the previous time)
• CUDA: 91.27248549461365s (0.15 x the previous time)

🚀

[Zartris]

Everything seems to work good.