Two Major Challenges in Reinforcement Learning Finally Addressed by Two ICLR Papers}

Real-time reinforcement learning is here! AI no longer suffers from lag.

Imagine a future where multiple chef robots collaborate to make an omelette. They need to act with perfect timing, monitor heat evenly, and adapt instantly to each other's actions. Any delay could ruin the dish. These robots must also handle uncertainties in their partners' actions and make immediate adjustments.

Real-time Reinforcement Learning

Current RL algorithms often rely on idealized interaction models where environment and agent take turns pausing for computation or response, which is unrealistic for real-world, continuous, delay-sensitive environments.

The figure highlights two key difficulties agents face in real-time environments, not encountered in standard turn-based RL:

• Long inference times can cause agents to skip actions, leading to inaction regret.
• Actions based on past states cause delays, resulting in delay regret, especially in stochastic environments.

In response, two papers from Mila's ICML 2025 collection propose a novel real-time RL framework to address inference delays and action omissions, enabling large models to respond instantly in high-frequency, continuous tasks.

Minimizing Inaction: Interleaved Inference

The first paper notes that in standard turn-based RL, larger models tend to have higher inaction. It proposes a new asynchronous multi-process inference and learning framework for real-world deployment.

• Paper link: https://openreview.net/pdf?id=YOc5t8PHf2
• Code repository: https://github.com/avecplezir/realtime-agent

The approach involves asynchronous reasoning and learning, allowing the agent to perform actions at every environment step regardless of model size or inference time, effectively eliminating inaction regret.

The framework was tested in Game Boy and Atari simulations, matching human-like frame rates and interaction protocols, demonstrating its effectiveness in fast-paced environments like Pokémon Blue, where rapid adaptation is crucial.

Additionally, the framework uses past actions and states to restore Markov properties, improving learning stability despite delays, and reducing both delay and optimization regret.

Combining Both Approaches

The interleaved asynchronous inference and temporal skip connections are complementary. Skip connections reduce internal delays, while interleaved inference ensures stable, rapid action output even with large models.

Together, they decouple model size from response latency, enabling deployment of powerful, fast-reacting agents in robotics, autonomous driving, and finance, where response speed is critical.

These methods mark a significant step toward real-world, delay-sensitive RL applications, allowing large models to make high-frequency decisions without sacrificing performance.

^Reference:

^{https://mila.quebec/en/article/real-time-reinforcement-learning}