In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …

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

Navigating toward specific objects in unknown environments without additional training,
known as Zero-Shot object navigation, poses a significant challenge in the field of robotics …

Abstract The Option Keyboard (OK) was recently proposed as a method for transferring
behavioral knowledge across tasks. OK transfers knowledge by adaptively combining …

This letter presents a novel algorithm for the continuous control of dynamical systems that
combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single …

Abstract Model-free reinforcement learning algorithms have exhibited great potential in
solving single-task sequential decision-making problems with high-dimensional …

Causality in an industrial process provides insights into how various process variables
interact and affect each other within the system. It reveals the underlying mechanisms of …

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

Abstract Model-based reinforcement learning (RL) methods are appealing in the offline
setting because they allow an agent to reason about the consequences of actions without …

Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with
solving sequential decision-making problems by a learning agent that interacts with the …