Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward.

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately.

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies.

To this end, we introduce Poppy, a simple training procedure for populations.

They had four primitive actions: north, south, east, and west. Multi-Floor Office is an extension of Office to multiple floors. Pick-up and put-down have the intended effect when appropriate; otherwise they do not change the state. T owers of Hanoi contains four discs of different sizes, placed on three poles. Options generated using alternative methods called primitive actions directly.

Imitation learning from human feedback studies how to train well-performed imitation agents with an annotator's relative comparison of two demonstrations