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arXiv · 1905.11100

Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction

Hongyao Tang·Jianye Hao·Guangyong Chen·Pengfei Chen·Zhaopeng Meng·Yaodong Yang·Li Wang
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Abstract

Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochasticity of environmental dynamics and the quality of reward signals. In this paper, we propose a two-step understanding of value estimation from the perspective of future prediction, through decomposing the value function into a reward-independent future dynamics part and a policy-independent trajectory return part. We then derive a practical deep RL algorithm from the above decomposition, consisting of a convolutional trajectory representation model, a conditional variational dynamics model to predict the expected representation of future trajectory and a convex trajectory return model that maps a trajectory representation to its return. Our algorithm is evaluated in MuJoCo continuous control tasks and shows superior results under both common settings and delayed reward settings.

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Hongyao Tang, Jianye Hao, Guangyong Chen, Pengfei Chen, Zhaopeng Meng, Yaodong Yang, Li Wang. 2019-05-27. Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction. https://arxiv.org/abs/1905.11100

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