Adaptive behavior often requires predicting future events. The theory of reinforcement
learning prescribes what kinds of predictive representations are useful and how to compute …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

Data representativity is crucial when drawing inference from data through machine learning
models. Scholars have increased focus on unraveling the bias and fairness in models, also …

Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …

Abstract Off-Policy Evaluation (OPE) in contextual bandits is crucial for assessing new
policies using existing data without costly experimentation. However, current OPE methods …

In this work, we study the use of the Bellman equation as a surrogate objective for value
prediction accuracy. While the Bellman equation is uniquely solved by the true value …

We consider the off-policy evaluation problem of reinforcement learning using deep
convolutional neural networks. We analyze the deep fitted Q-evaluation method for …

Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework
has been proposed as a method for learning, composing, and transferring predictive …

Lifelong deep reinforcement learning (DRL) approaches are commonly employed to adapt
continuously to new tasks without forgetting previously acquired knowledge. While current …

In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS)
with incremental number of agents is studied. The existing multi-agent reinforcement …