GaLiTe and AGaLiTe: Efficient Transformer Alternatives for Partially Observable Online Reinforcement Learning

In real-world settings, agents often face limited visibility of the environment, complicating decision-making. For instance, a car-driving agent must recall road signs from moments earlier to adjust its speed, yet storing all observations is unscalable due to memory limits. Instead, agents must learn compressed representations of observations. This challenge is compounded in ongoing tasks, where […]

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