tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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Tibo De Peuter 2026-03-15 23:00:30 +01:00 • committed by GitHub
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docs/design/reward_function.md
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@ -4,9 +4,9 @@ The robot needs to know whether its movements contribute to the ultimate goal of
inputs must be distributed fairly to guarantee an objective comparison between different architectures.
- The distance from the robot to the target and/or the light intensity are treated as global inputs.
- Positions and joints, normalized to floating-point values between 0 and 1 are considered local inputs.
- Positions and joints, which are normalized to floating-point values between 0 and 1, are considered local inputs.
- The reward function is centered around minimizing the distance to the goal or maximizing the movement towards the goal
within a finite number of timesteps $T$.
within a finite number of timesteps $T$.
## Rationale