A reward-free human-in-the-loop RL method that labels human demonstrations with high Q values and intervened agent actions with low Q values, then propagates these values through TD learning to train policies across driving and gridworld tasks.
Minimalistic gridworld environment for openai gym
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Learning from Active Human Involvement through Proxy Value Propagation
A reward-free human-in-the-loop RL method that labels human demonstrations with high Q values and intervened agent actions with low Q values, then propagates these values through TD learning to train policies across driving and gridworld tasks.