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Beyond Turn-Based AI: Introducing Real-Time Reinforcement Learning

Picture this: a team of robotic chefs collaborates to craft the perfect omelet. For this to work, they need more than just powerful AI models; they need models that can keep up with the frantic, ever-changing pace of a real kitchen. Ingredients must be added at precise moments, and the heat needs constant adjustment. The slightest delay from one robot could mean a burnt omelet for everyone. To succeed, they must anticipate each other's moves and make split-second adjustments, turning chaos into culinary harmony.

The Problem with "AI Lag"

Unfortunately, most contemporary reinforcement learning (RL) algorithms aren't built for this kind of real-time pressure. They operate on an idealized, turn-based model, like players in a board game. The environment makes a move, then pauses. The AI agent thinks, learns, and then makes its move. This back-and-forth relies on two flawed assumptions:

• The Environment Pause: The world is assumed to stand still while the agent computes its next action and learns from the past.
• The Agent Pause: The agent is assumed to halt its decision-making process while the environment transitions to a new state.

This "turn-based" paradigm is a far cry from reality, where the world doesn't wait. For applications in dynamic, latency-sensitive fields, this model simply breaks down.

The diagram below illustrates two critical challenges an agent faces in a real-time setting—challenges that standard RL research often overlooks.

First, if an agent's model is complex, the time it takes to "infer" the best action can be longer than a single time-step in the environment. This means the agent might miss its chance to act, forcing it into a suboptimal strategy of simply doing nothing. We call this phenomenon inaction regret.

Second, by the time an action is finally executed, the state of the world has already changed. The action is based on old information, making it less effective. This mismatch, especially in unpredictable environments, creates another form of suboptimality we term delay regret.

Tackling these issues head-on, two ICLR 2025 papers from the Mila - Quebec AI Institute propose a groundbreaking framework for real-time reinforcement learning. Their work aims to eliminate the inference latency and missed actions that plague current RL systems, enabling even massive models to respond instantaneously in high-frequency, continuous tasks.

The first paper introduces a solution to minimize inaction regret, while the second tackles delay regret.

Solution 1: Minimizing Inaction with Interleaved Inference

The first paper confronts a critical bottleneck: as AI models get bigger, their decision-making time increases, leading to more frequent inaction. To deploy large-scale foundation models in the real world, the RL community needs a new approach. The paper delivers one with a framework for asynchronous, multi-process inference and learning.

In this framework, the agent leverages all available computing power to think and learn in parallel. The paper introduces two "interleaved inference" algorithms. The core idea is to run multiple inference processes simultaneously but stagger their start times. Think of it like multiple production lines for decisions; by staggering them, a fully-formed decision rolls off the line at a steady, rapid interval, ready for the environment.

The authors prove that with sufficient computational resources, these algorithms allow an agent to execute an action at every single environment step, no matter how large the model or how long its inference time. This effectively eliminates inaction regret.

To validate this, the framework was tested in real-time Game Boy and Atari simulations, with frame rates and interaction protocols synced to match a human player's experience.

The results in the game Pokémon Blue were particularly striking. A massive 100-million-parameter model not only acted quickly enough to succeed but also continuously adapted to new scenarios to capture a Pokémon.

Furthermore, tests in reaction-sensitive games like Tetris showed that while using asynchronous inference, larger models degraded in performance more gracefully. However, this pointed to the remaining culprit: delay regret, which this first approach doesn't fully solve.

Solution 2: Tackling Inaction and Delay with a Single Neural Network

The second paper proposes an elegant architectural solution to minimize both inaction and delay, especially in scenarios where interleaved inference isn't feasible. The problem with standard deep neural networks is their sequential nature. Each layer processes data one after another, and the total latency is the sum of all layer computation times. The deeper the network, the slower the response.

This bottleneck is reminiscent of early CPU architectures, where processing instructions serially created performance logjams. Modern CPUs solved this with pipelining, a technique that executes different stages of multiple instructions in parallel.

Inspired by this, the paper introduces a pipelining mechanism directly into the neural network. By computing all network layers simultaneously, it dramatically increases the rate of action output, effectively crushing inaction regret.

To then conquer delay, the paper introduces temporal skip connections. These act like express lanes on a highway, allowing fresh data from the environment to bypass intermediate layers and reach the final decision-making layer almost instantly.

The core innovation is the fusion of these two concepts—parallel computation and temporal skip connections—to simultaneously slash both inaction and delay regret.

The diagram below breaks it down. The vertical axis represents the network's layers, from input observation to final action. The horizontal axis is time. Each arrow represents one layer's computation, which takes a fixed time, δ.

• Baseline (Left): A new observation must pass through all N layers sequentially. The final action is only available after N × δ seconds.
• Pipelined (Center): By parallelizing the layers, the model can output a new action every δ seconds instead of every Nδ seconds. This boosts throughput and reduces inaction.
• Pipelined + Skip Connections (Right): Temporal skip connections reduce the total latency from observation to action all the way down to a single δ. The newest information gets a fast track to the output, fundamentally solving the delay problem by balancing the network's depth with the need for timely information.

Additionally, by feeding past actions and states back into the input, the model can better account for delays and maintain what's known as the Markov property, leading to more stable and effective learning. As the results confirm, this approach reduces regret from both delay and optimization issues.

Combining Both Approaches for Ultimate Real-Time Performance

Think of these two solutions not as competitors, but as powerful allies. Temporal skip connections minimize the internal latency within the model, while interleaved inference guarantees a steady stream of actions from the model.

When used together, they decouple model size from interaction speed. This makes it possible to deploy agents that are both incredibly intelligent (expressive) and incredibly fast (responsive). This synergy unlocks enormous potential for high-stakes domains where reaction speed is everything, such as robotics, autonomous driving, and high-frequency financial trading.

By enabling even the largest AI models to make high-frequency decisions without compromise, these methods mark a crucial leap forward in bringing reinforcement learning out of the lab and into the real world.

Reference: https://mila.quebec/en/article/real-time-reinforcement-learning