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

Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.

Agents that exist persistently in the world can prepare to solve diverse tasks by setting their own goals, practicing complex behaviors, and learning about the world around them.

What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization's answer to this question is to use rectangular

V alue-A ware Model Learning (V AML) is a novel framework for learning the model of the environment in Model-Based Reinforcement Learning (MBRL) [Farahmand et al., 2017a, 2016a].

We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.

Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.

In many settings (e.g., robotics) demonstrations provide a natural way to specify a task. For example, an agent (e.g., human expert) gives one or more demonstrations of the task from which we seek to automatically synthesize a policy for the robot to execute. Typically, one models the demonstrator as episodically operating within a dynamical system whose transition relation only depends on the current state and action (called the Markov condition). However, even if the dynamics are Markovian, many problems are naturally modeled in non-Markovian terms (see Ex 1).

The planners use representation-specific algorithms, thus allowing themselves to be run on all domains that can be expressed in the representation.

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