In this paper, we take a step toward narrowing the gap between model-based and model-free RL methods.

Diversity in equally-performing policies.We show that different neighborhoods correspond to different post-update return distributions and agent behaviors. We discover that at equal average returns, different policies obtained by the same deep RL algorithm may in fact have substantially different distributional profiles, as measured by statistics of the post-update return distribution.

Recently, reinforcement learning (RL) based approaches have attracted increasing attention for dynamic resource management asRLhelpsautomatically adapttoaspecific userworkload.

Reinforcement learning (RL) [1] studies the problem of learning in sequential decision-making problems where the dynamics of the environment is unknown, but can be learnt by performing actions andobserving their outcome inanonline fashion. Asample-efficient RLagent must trade off the explorationneeded to collect information about the environment, and theexploitation of the experience gathered so far to gain as much reward as possible.

Neural Information Processing Systems http://nips.cc/