Learning from Active Human Involvement through Proxy Value Propagation

要約

積極的な人間の関与から学ぶことで、人間の被験者は積極的に介入し、トレーニング中にAIエージェントに実証することができます。
人間からの相互作用と修正フィードバックは、学習プロセスに安全とAIの整合性をもたらします。
この作業では、ポリシーの最適化のためのプロキシバリュー伝播と呼ばれる新しい報酬のないアクティブな人間の関与方法を提案します。
Our key insight is that a proxy value function can be designed to express human intents, wherein state-action pairs in the human demonstration are labeled with high values, while those agents’ actions that are intervened receive low values.
Through the TD-learning framework, labeled values of demonstrated state-action pairs are further propagated to other unlabeled data generated from agents’ exploration.
したがって、プロキシ値関数は、人間の行動を忠実にエミュレートするポリシーを誘導します。
人間のループ実験は、私たちの方法の一般性と効率性を示しています。
With minimal modification to existing reinforcement learning algorithms, our method can learn to solve continuous and discrete control tasks with various human control devices, including the challenging task of driving in Grand Theft Auto V. Demo video and code are available at: https://
metadriverse.github.io/pvp

要約(オリジナル)

Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method called Proxy Value Propagation for policy optimization. Our key insight is that a proxy value function can be designed to express human intents, wherein state-action pairs in the human demonstration are labeled with high values, while those agents’ actions that are intervened receive low values. Through the TD-learning framework, labeled values of demonstrated state-action pairs are further propagated to other unlabeled data generated from agents’ exploration. The proxy value function thus induces a policy that faithfully emulates human behaviors. Human-in-the-loop experiments show the generality and efficiency of our method. With minimal modification to existing reinforcement learning algorithms, our method can learn to solve continuous and discrete control tasks with various human control devices, including the challenging task of driving in Grand Theft Auto V. Demo video and code are available at: https://metadriverse.github.io/pvp

arxiv情報

提供元, 利用サービス

arxiv.jp, Google