Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou

TL;DR

This work tackles real-time reinforcement learning under observational delay caused by parallel neural computation. By introducing temporal skip connections and history-augmented observations, the authors reduce delay-induced regret while maintaining, and often enhancing, policy expressivity. They validate the approach across MuJoCo, MinAtar, and MiniGrid, showing strong performance and substantial inference-time speed-ups on GPUs (up to $350\%$) with modest trade-offs in more complex environments. The results establish a practical pathway for efficient, real-time RL agents and outline limitations and future directions, including stochastic delays and scaling to deeper architectures.

Abstract

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of $τ$, an $N$-layer feed-forward network experiences observation delay of $τN$. Reducing the number of layers can decrease this delay, but at the cost of the network's expressivity. In this work, we explore the trade-off between minimizing delay and network's expressivity. We present a theoretically motivated solution that leverages temporal skip connections combined with history-augmented observations. We evaluate several architectures and show that those incorporating temporal skip connections achieve strong performance across various neuron execution times, reinforcement learning algorithms, and environments, including four Mujoco tasks and all MinAtar games. Moreover, we demonstrate parallel neuron computation can accelerate inference by 6-350% on standard hardware. Our investigation into temporal skip connections and parallel computations paves the way for more efficient RL agents in real-time setting.

Handling Delay in Real-Time Reinforcement Learning

TL;DR

This work tackles real-time reinforcement learning under observational delay caused by parallel neural computation. By introducing temporal skip connections and history-augmented observations, the authors reduce delay-induced regret while maintaining, and often enhancing, policy expressivity. They validate the approach across MuJoCo, MinAtar, and MiniGrid, showing strong performance and substantial inference-time speed-ups on GPUs (up to ) with modest trade-offs in more complex environments. The results establish a practical pathway for efficient, real-time RL agents and outline limitations and future directions, including stochastic delays and scaling to deeper architectures.

Abstract

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of , an -layer feed-forward network experiences observation delay of . Reducing the number of layers can decrease this delay, but at the cost of the network's expressivity. In this work, we explore the trade-off between minimizing delay and network's expressivity. We present a theoretically motivated solution that leverages temporal skip connections combined with history-augmented observations. We evaluate several architectures and show that those incorporating temporal skip connections achieve strong performance across various neuron execution times, reinforcement learning algorithms, and environments, including four Mujoco tasks and all MinAtar games. Moreover, we demonstrate parallel neuron computation can accelerate inference by 6-350% on standard hardware. Our investigation into temporal skip connections and parallel computations paves the way for more efficient RL agents in real-time setting.

Paper Structure

This paper contains 36 sections, 3 theorems, 7 equations, 15 figures, 11 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Setting and Notation
  4. Formalizing the Parallel Computation Framework
  5. Sources of Realtime Regret
  6. Method
  7. Addressing Delay
  8. Addressing Training Stability
  9. Algorithm
  10. Experiments
  11. Main Results
  12. Ablation Study
  13. Disentangling architectural benefits of skip connections.
  14. Analysis
  15. Inference Time Speed-Up
  16. ...and 21 more sections

Key Result

Proposition 1

(Tighter Delay Regret Bound): For any vanilla $N$ layer neural network without temporal skip connections in parallel computation framework, the regret resulting from delay $\Delta^\text{vanilla}_{\text{delay}}(t)$ after $t$ steps in a worst case environment can be lower bounded by: where $p_\text{minimax} :=\min_{s \in \mathcal{S}, a \in \mathcal{A}} \max_{s' \in \mathcal{S}} p(s'|s,a)$ is a meas

Figures (15)

  • Figure 1: (a) Parallel computations of layers speed-up inference time. Speed-up on GPU is achieved using default Pytorch software and widely accessible Nvidia GPU. (b) Normalized averaged performance and standard error of agents in parallel computation framework. Agents with skip connections and history-augmented observations exhibit strong performance. Performance is averaged across the following environments: HalfCheetah-v4, Walker2d-v4, Ant-v4 and Hopper-v4 on Mujoco, all six environments on MinAtar, and Empty-Random-5x5-v0 and DoorKey-5x5-v0 on MiniGrid. Performance on Mujoco is also averaged across four different neuron execution times.
  • Figure 2: Computation flow of agents. Left graph represents sequential computations and the central graph -- parallel computations of layers. $\delta$ is execution time of each neuron (or layer). All nodes at each column are available at the same time and can be processed further in parallel. The right architecture with skip connections exhibits less delay as it performs shortcuts along time-steps.
  • Figure 3: The performance of different agents and RLRD method on Mujoco. The agent with skip connections performs as well as, or better than, other agents in general. SAC without delay, which has a normalized performance of one, is omitted from the plots. The shaded area indicates SE across 3 seeds.
  • Figure 4: Illustration of different skip connections. $\delta$ represents execution time of each neuron.
  • Figure 5: Removing different connections in the proj-to-action agent. Mean and one SD across 100 episodes are reported.
  • ...and 10 more figures

Theorems & Definitions (3)

  • Proposition 1
  • Proposition 2
  • Proposition 3