Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

TL;DR

The paper tackles real-time reinforcement learning where environmental dynamics continue while an agent infers actions, making large, accurate models impractical under sequential interaction. It introduces a staggered, multi-process framework that decouples environment, inference, and learning rates, formalizes this with an induced delayed semi-MDP, and analyzes realtime regret decomposed into learning, inaction, and delay components. The authors prove lower bounds showing sequential setups incur persistent regret with large models, and present two practical algorithms (max_time and exp_time) to stagger inference, achieving linear speedups and enabling models orders of magnitude larger to operate in realtime settings. Empirical results in Game Boy-inspired simulations (Pokemon, Tetris) and Atari confirm the theoretical insights, showing maintained or improved performance as model size grows when using asynchronous staggering. This work provides a scalable pathway for deploying large-scale, realtime foundation models in interactive environments and highlights hardware and software requirements to realize these gains.

Abstract

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectively minimize regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where reaction time is crucial. We present an analysis of lower bounds on regret in realtime reinforcement learning (RL) environments to show that minimizing long-term regret is generally impossible within the typical sequential interaction and learning paradigm, but often becomes possible when sufficient asynchronous compute is available. We propose novel algorithms for staggering asynchronous inference processes to ensure that actions are taken at consistent time intervals, and demonstrate that use of models with high action inference times is only constrained by the environment's effective stochasticity over the inference horizon, and not by action frequency. Our analysis shows that the number of inference processes needed scales linearly with increasing inference times while enabling use of models that are multiple orders of magnitude larger than existing approaches when learning from a realtime simulation of Game Boy games such as Pokémon and Tetris.

Figures (14)

• Figure 1: Frameworks for Environment Interaction in RL. a) The typical sequential interaction paradigm where both learning and action inference block the environment from moving forward. b) The more realistic setting considered in this work where the environment, the agent's inference process, and agent's learning process all proceed at their own rate and interact asynchronously. Multiple self-loops are depicted to denote multiple asynchronous processes. $\tau_\mathcal{M}$ denotes the frequency of the environment, $\tau_\theta$ denotes the frequency of each inference process, and $\tau_\mathcal{L}$ denotes the frequency of each learning process. Sequential interaction and learning has a frequency of $\tau_\mathcal{M}+ \tau_\theta+ \tau_\mathcal{L}$.
• Figure 2: Induced Delayed Semi-MDP. We illustrate the semi-MDP described in Definition \ref{['def:delayed_semi_mdp']} following the style of Figure 1 from sutton1999between. $\mathcal{M}_\text{async}$ is depicted in purple and $\tilde{\mathcal{M}}_\text{delay}$ is depicted in blue. Actions are delayed by the inference time of the policy $\pi$ and the default policy $\beta$ is followed between selections.
• Figure 3: Realtime Interaction Frequency. We illustrate the comparative interaction frequency of methods that sequence learning and inference and those that maintain multiple staggered asynchronous processes. Even when inference times are greater than the environment step time, it is possible to use asynchronous compute to eliminate inaction and learn from every step.
• Figure 4: Realtime Pokémon Performance. a) Battles won in Pokémon Blue over time for $|\theta|=100M$. b) Wild Pokémon caught in Pokémon Blue over time for $|\theta|=100M$. The parallel learning baseline considers an effective batch size that is $33$ times larger with $33$ times fewer updates.
• Figure 5: Realtime Tetris Performance vs. $|\theta|$. The average episodic return over 2,000 episodes of learning. We compare models with a single inference process to those that perform staggered asynchronous inference following Algorithm \ref{['alg:max_time']}.

Theorems & Definitions (4)

• Definition 1: Induced Delayed Semi-MDP
• Theorem 1: Realtime Regret Decomposition
• Remark 1: Inaction of Sequential Interaction
• Remark 2: Inaction of Asynchronous Interaction