Partial observability refers to a situation in which an agent does not have complete information about the environment it operates in. This means that the agent must make decisions based on limited or noisy observations, leading to uncertainty and potentially suboptimal choices. In reinforcement learning, this can complicate the learning process since the agent cannot fully understand the consequences of its actions in every state.
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