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+# Reward function and observation space
+
+The robot needs to know whether its movements contribute to the ultimate goal of locomotion towards a target. Sensor
+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.
+- 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 $T$.
+
+## Rationale
+
+Using a light source (or a gradient) is biologically plausible for many simple organisms. By normalizing all signals
+between 0 and 1, PPO training is highly stabilized. The timesteps must be finite to reset the environment in a timely
+manner if the policy gets stuck in a local minimum.
+
+## Limitations and alternatives
+
+Providing global information to all individual decentralized segments can be considered biologically cheating or
+practically infeasible once the robot would be physically built. Some sensory input cannot be put in each joint, for
+example.
+
+The alternative is to provide the global input to the outermost segments of the arms, or a specific set of segments
+assigned with this functionality. The network would then have to learn to propagate this signal throughout the body via
+message passing. While biologically more accurate, this drastically complicates the learning process. We have written
+this down as potential future research.