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Poster
Neural Temporal-Difference Learning Converges to Global Optima
Qi Cai · Zhuoran Yang · Jason Lee · Zhaoran Wang

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #211
in Reinforcement Learning and Planning -- Reinforcement Learning »

Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, due to the nonlinearity in value function approximation, such a coupling leads to nonconvexity and even divergence in optimization. As a result, the global convergence of neural TD remains unclear. In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for policy evaluation. In particular, we show how such global convergence is enabled by the overparametrization of neural networks, which also plays a vital role in the empirical success of neural TD. Beyond policy evaluation, we establish the global convergence of neural (soft) Q-learning, which is further connected to that of policy gradient algorithms.

Keywords: Multi-Agent RL · Non-Convex Optimization · Optimization · Reinforcement Learning and Planning
[ Paper]  [ Slides] 

Author Information

Qi Cai (Northwestern University)
Zhuoran Yang (Princeton University)
Jason Lee (Princeton University)
Zhaoran Wang (Northwestern University)

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