It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms.

Despite this, abstraction remains poorly understood.

We study the problem of off-policy policy evaluation (OPPE) in RL.

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

There also exist many application scenarios involving multiagent interactions, commonly known as multiagent systems (MAS).

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

Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning.

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

It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we

We prove that the proposed algorithm converges to the optimal solution at a global geometric rate.