Bernhard add mean action - #541
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…ent is continuous.
… into bernhard_01
…rything mode for now.
… into bernhard_01
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vcharraut
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Jul 27, 2026
vcharraut
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Jul 27, 2026
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| self.register_buffer( | ||
| "JERK_LONG", | ||
| torch.tensor((-15.0, -4.0, 0.0, 4.0), dtype=torch.float32, requires_grad=False), | ||
| persistent=False, | ||
| ) | ||
| self.register_buffer( | ||
| "JERK_LAT", torch.tensor((-4.0, 0.0, 4.0), dtype=torch.float32, requires_grad=False), persistent=False | ||
| ) | ||
|
|
||
| self.register_buffer( | ||
| "ACCELERATION_VALUES", | ||
| torch.tensor( | ||
| (-4.0000, -2.6670, -1.3330, -0.0000, 1.3330, 2.6670, 4.0000), dtype=torch.float32, requires_grad=False | ||
| ), | ||
| persistent=False, | ||
| ) | ||
| self.register_buffer( | ||
| "STEERING_VALUES", | ||
| torch.tensor( | ||
| (-0.667, -0.500, -0.333, -0.167, 0.000, 0.167, 0.333, 0.500, 0.667), | ||
| dtype=torch.float32, | ||
| requires_grad=False, | ||
| ), | ||
| persistent=False, | ||
| ) | ||
|
|
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theses values should come directly from the BINDING defined in env_binding.h
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why do you think that? These are method-specific hyperparameters.
Maybe the min and max possible values should come from the environment?
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Maybe the min and max possible values should come from the environment? -> Yes, should have been more specific
// Jerk action space (for JERK dynamics model)
static const float JERK_LONG[4] = {-15.0f, -4.0f, 0.0f, 4.0f};
static const float JERK_LAT[3] = {-4.0f, 0.0f, 4.0f};
// Classic action space (for CLASSIC dynamics model)
static const float ACCELERATION_VALUES[7] = {-4.0000f, -2.6670f, -1.3330f, -0.0000f, 1.3330f, 2.6670f, 4.0000f};
static const float STEERING_VALUES[9] = {-0.667f, -0.500f, -0.333f, -0.167f, 0.000f, 0.167f, 0.333f, 0.500f, 0.667f};We direclty want to use these variables here to avoid duplicate and potential errors
vcharraut
reviewed
Jul 27, 2026
| self.is_continuous = isinstance(env.single_action_space, pufferlib.spaces.Box) | ||
| self.is_continuous = ( | ||
| action_type == "continuous" | ||
| ) # TODO Check if what the `"trajectory"`, `"trajectory_frenet"`, `"trajectory_jerk" features do and if they are considered continuous. |
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you can remove the comment about traj, it is a mistake from the README.md
vcharraut
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Jul 27, 2026
| action = logits.sample().view(batch, -1) | ||
| if deterministic: | ||
| if action_selection != ACTION_SELECT_SAMPLE: | ||
| action = logits.loc.view(batch, -1) # TODO - DETERMINISTIC use mean action for eval |
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this comment seems obselete now
vcharraut
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Jul 27, 2026
vcharraut
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Jul 27, 2026
vcharraut
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Jul 27, 2026
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The same PR as 536 before, but without my personal files:
Added the option to make the policy use either the mean, the mode, or a sample of the distribution instead of the mode during inference.
This required a fundamental design change.
Previously, the action representation was hardcoded in the simulator.
Now there are two options: one for the agent, either continuous or discrete, and one for the environment, either continuous or discrete (to avoid removing features).
The default now is:
env.action_type: continuous
policy.action_type: discrete
The default for eval is kept in mode so that there is no change in behavior with the default configuration.
In local eval eval_simulation: gigaflow
and default parameters, this improved the latest nightly model slightly:
mean: "avg_distance_per_infraction": 16646
mode: "avg_distance_per_infraction": 16127
I verified that these changes have no influence on training performance.