Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unlabelled snapshots of the states to decode state pairs into actions. However, the iterative learning scheme employed by these techniques is prone to get trapped into bad local minima. Previous work uses goal-aware strategies to solve this issue. However, this requires manual intervention to verify whether an agent has reached its goal. We address this limitation by incorporating a discriminator into the original framework, offering two key advantages and directly solving a learning problem previous work had. First, it disposes of the manual intervention requirement. Second, it helps in learning by guiding function approximation based on the state transition of the expert's trajectories. Third, the discriminator solves a learning issue commonly present in the policy model, which is to sometimes perform a `no action' within the environment until the agent finally halts.

翻译：行为克隆是一种通过专家演示教会智能体如何行为的模仿学习技术。近期方法利用完全可观察的未标注状态快照进行自监督，将状态对解码为动作。然而，这些技术采用的迭代学习机制容易陷入不良局部最小值。先前工作使用目标感知策略解决此问题，但这需要人工干预来验证智能体是否达到目标。我们通过将判别器融入原始框架来解决这一局限，提供了两个关键优势并直接解决了前人工作中的学习问题：第一，它消除了人工干预的需求；第二，它通过基于专家轨迹状态转移指导函数近似来辅助学习；第三，判别器解决了策略模型中常见的学习问题——即有时会在环境中执行'无动作'直到智能体最终停止。