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Deployable Reinforcement Learning with Variable Control Rate

Dong Wang, Giovanni Beltrame

TL;DR

The paper tackles the inefficiency of fixed-time-step RL in resource‑constrained robotics by introducing a variable control rate framework. It presents Soft Elastic Actor-Critic (SEAC), which extends SAC to jointly predict actions and the duration of the next time step, in line with reactive programming principles. A multi‑objective reward balances task performance with computational energy and time cost, implemented in a Newtonian kinematics verification environment; SEAC outperforms fixed‑rate SAC and PPO in average return, task time, and energy usage, and exhibits improved data efficiency. This approach promises greater deployability of RL on onboard hardware by reducing unnecessary computation while preserving or enhancing control performance.

Abstract

Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), which assume an inherently discrete passage of time. The use of MDPs results in that nearly all RL-based control systems employ a fixed-rate control strategy with a period (or time step) typically chosen based on the developer's experience or specific characteristics of the application environment. Unfortunately, the system should be controlled at the highest, worst-case frequency to ensure stability, which can demand significant computational and energy resources and hinder the deployability of the controller on onboard hardware. Adhering to the principles of reactive programming, we surmise that applying control actions only when necessary enables the use of simpler hardware and helps reduce energy consumption. We challenge the fixed frequency assumption by proposing a variant of RL with variable control rate. In this approach, the policy decides the action the agent should take as well as the duration of the time step associated with that action. In our new setting, we expand Soft Actor-Critic (SAC) to compute the optimal policy with a variable control rate, introducing the Soft Elastic Actor-Critic (SEAC) algorithm. We show the efficacy of SEAC through a proof-of-concept simulation driving an agent with Newtonian kinematics. Our experiments show higher average returns, shorter task completion times, and reduced computational resources when compared to fixed rate policies.

Deployable Reinforcement Learning with Variable Control Rate

TL;DR

The paper tackles the inefficiency of fixed-time-step RL in resource‑constrained robotics by introducing a variable control rate framework. It presents Soft Elastic Actor-Critic (SEAC), which extends SAC to jointly predict actions and the duration of the next time step, in line with reactive programming principles. A multi‑objective reward balances task performance with computational energy and time cost, implemented in a Newtonian kinematics verification environment; SEAC outperforms fixed‑rate SAC and PPO in average return, task time, and energy usage, and exhibits improved data efficiency. This approach promises greater deployability of RL on onboard hardware by reducing unnecessary computation while preserving or enhancing control performance.

Abstract

Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), which assume an inherently discrete passage of time. The use of MDPs results in that nearly all RL-based control systems employ a fixed-rate control strategy with a period (or time step) typically chosen based on the developer's experience or specific characteristics of the application environment. Unfortunately, the system should be controlled at the highest, worst-case frequency to ensure stability, which can demand significant computational and energy resources and hinder the deployability of the controller on onboard hardware. Adhering to the principles of reactive programming, we surmise that applying control actions only when necessary enables the use of simpler hardware and helps reduce energy consumption. We challenge the fixed frequency assumption by proposing a variant of RL with variable control rate. In this approach, the policy decides the action the agent should take as well as the duration of the time step associated with that action. In our new setting, we expand Soft Actor-Critic (SAC) to compute the optimal policy with a variable control rate, introducing the Soft Elastic Actor-Critic (SEAC) algorithm. We show the efficacy of SEAC through a proof-of-concept simulation driving an agent with Newtonian kinematics. Our experiments show higher average returns, shorter task completion times, and reduced computational resources when compared to fixed rate policies.
Paper Structure (10 sections, 8 equations, 8 figures, 3 tables)

This paper contains 10 sections, 8 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Reinforcement Learning with A Fixed Control Rate
  3. Reinforcement Learning with Variable Control Rate
  4. Energy and Time Concerned Reward Policy
  5. The Verification Environment Design
  6. Implementation of The SEAC
  7. Experimental Results
  8. Conclusions and Future Work
  9. Appendix:A Validation Environment Details
  10. Appendix:B Hyperparameter Setting of SEAC

Figures (8)

  • Figure 1: Comparing Elastic Time Step Reinforcement Learning and Traditional Reinforcement Learning
  • Figure 2: (a) the reward policy for traditional RL; (b) the reward policy for elastic time step RL
  • Figure 3: A simple Newtonian Kinematics environment designed for verifying SEAC based on gymnasium.
  • Figure 4: (a) The SEAC ActorNetwork Architecture; (b) The SAC ActorNetwork Architecture.
  • Figure 5: Average returns for three algorithms trained in 1.2 million steps. The figure on the right is a partially enlarged version of the figure on the left.
  • ...and 3 more figures

Theorems & Definitions (4)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4