Why should continuous actions be clamped?

Question

In Deep Reinforcement Learning, using continuous action spaces, why does it seem to be common practice to clamp the action right before the agent's execution?

Examples:

OpenAI Gym Mountain Car https://github.com/openai/gym/blob/master/gym/envs/classic_control/continuous_mountain_car.py#L57

Unity 3DBall https://github.com/Unity-Technologies/ml-agents/blob/master/unity-environment/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs#L29

Isn't information lost doing so? Like if the model outputs +10 for velocity (moving), which is then clamped to +1, the action itself behaves rather discrete (concerning its mere execution). For a fine grained movement, wouldn't it make more sense to multiply the output by something like 0.1?

Answer 1

This is probably simply done to enforce constraints on what the agent can do. Maybe the agent would like to put out an action that increases velocity by 1,000,000. But if the agent is a self-driving car with a weak engine that can at most accelerate by 1 unit, we don't care if the agent would hypothetically like to accelerate by more units. The car's engine has limited capabilities.