The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
This work addresses sample efficiency issues in deep reinforcement learning for researchers and practitioners, though it is incremental as it builds on existing methods with a novel termination strategy.
The paper tackles the problem of inefficient sampling in off-policy deep reinforcement learning by addressing the sunk cost fallacy, proposing a lightweight mechanism called LEAST that enables strategic early episode termination, which improves learning efficiency on benchmarks like MuJoCo and DeepMind Control Suite.
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help improve sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of "polluting" the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.