<ul><li>Real-time reinforcement learning (RL) involves challenges such as limited actions per second and observational delay.</li><li>Pipelining can address the limited actions issue, improving throughput and potential policy quality.</li><li>To tackle observational delay, a solution that leverages temporal skip connections and history-augmented observations is proposed.</li><li>Architectures with temporal skip connections achieve strong performance and parallel neuron computation can accelerate inference.</li></ul>

Handling Delay in Real-Time Reinforcement Learning

Discover more