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

In this paper, we consider the problem of determining when along a training roll-out feedback from the environment is no longer beneficial, and an intervention such as resetting the agent to the initial state distribution is warranted. We show that such interventions can naturally trade off a small sub-optimality gap for a dramatic decrease in sample complexity. In particular, we focus on the reinforcement learning setting in which the agent has access to a reward signal in addition to either (a) an expert supervisor triggering the e-stop mechanism in real-time or (b) expert state-only demonstrations used to "learn" an automatic e-stop trigger.

However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on

Meta-reinforcement learning and multi-task reinforcement learning aim to improve the sample efficiency by leveraging the shared structure within a family of tasks.

These have the opposite properties, with DRL having good sample efficiency and poor stability, while ES being vice versa.

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

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management.

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