When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …

Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic
information about good policies can be extracted from offline data which lack explicit …

The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term …

Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The
program of an RNN is its weight matrix. How to learn useful representations of RNN weights …

Abstract Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies,
given goals encoded in special command inputs. Here we study goal-conditioned neural …

Abstract We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement
Learning (RL), which extends conventional value function approximator (VFA) to take as …

Trading networks are an indispensable part of today's economy, but to compete successfully
with others, they must be efficient in maximizing the value they provide to the external …

Learning to evaluate and improve policies is a core problem of Reinforcement Learning
(RL). Traditional RL algorithms learn a value function defined for a single policy. A recently …

Efficient exploration in reinforcement learning is a challenging problem commonly
addressed through intrinsic rewards. Recent prominent approaches are based on state …

There are two important things in science:(A) Finding answers to given questions, and (B)
Coming up with good questions. Our artificial scientists not only learn to answer given …