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Data Efficient Deep Reinforcement Learning With Action-Ranked Temporal Difference Learning
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DOI：10.1109/TETCI.2024.3369641.png)
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Abstract
En 中文
In value-based deep reinforcement learning (RL), value function approximation errors lead to suboptimal policies. Temporal difference (TD) learning is one of the most important methodologies to approximate state-action (Q) value function. In TD learning, it is critical to estimate Q values of greedy actions more accurately because a more accurate target Q value enhances the estimation accuracy of Q value. To improve the estimation accuracy of Q value, we propose an action-ranked TD learning method to enhance the performance of deep RL by weighting each TD error according to the rank of its corresponding state-action pair's value among all the Q values on a state. The proposed method can provide more accurate target values for TD learning, making the estimation of the Q value more accurate. We apply the proposed method to a representative value-based deep RL algorithm, and results show that the proposed method outperforms baselines on 31 out of 40 Atari games. Furthermore, we extend the proposed method to multi-agent deep RL. To adaptively determine the hyperparameter in action-ranked TD learning, we propose a meta action-ranked TD learning. A series of experiments quantitatively verify that our methods outperform baselines on Atari games, StarCraft-II, and Grid World environments.
Keywords:
Reinforcement learning
temporal difference
data efficient
action-rank
meta learning
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6.5
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1.4K
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4.5K
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