Tactical Optimism and Pessimism for Deep Reinforcement Learning. (arXiv:2102.03765v4 [cs.LG] UPDATED)

In recent years, deep off-policy actor-critic algorithms have become a
dominant approach to reinforcement learning for continuous control. One of the
primary drivers of this improved performance is the use of pessimistic value
updates to address function approximation errors, which previously led to
disappointing performance. However, a direct consequence of pessimism is
reduced exploration, running counter to theoretical support for the efficacy of
optimism in the face of uncertainty. So which approach is best? In this work,
we show that …

arxiv learning optimism reinforcement learning