Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
Recent advances in robot learning from demonstration
H Ravichandar, AS Polydoros… - Annual review of …, 2020 - annualreviews.org
In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
Defining and characterizing reward gaming
We provide the first formal definition of\textbf {reward hacking}, a phenomenon where
optimizing an imperfect proxy reward function, $\mathcal {\tilde {R}} $, leads to poor …
optimizing an imperfect proxy reward function, $\mathcal {\tilde {R}} $, leads to poor …
Contrastive learning as goal-conditioned reinforcement learning
In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …
While deep RL should automatically acquire such good representations, prior work often …
An introduction to deep reinforcement learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …
learning. This field of research has been able to solve a wide range of complex …
[HTML][HTML] The free energy principle made simpler but not too simple
This paper provides a concise description of the free energy principle, starting from a
formulation of random dynamical systems in terms of a Langevin equation and ending with a …
formulation of random dynamical systems in terms of a Langevin equation and ending with a …
Robust reinforcement learning: A review of foundations and recent advances
Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Abstract Model-free deep reinforcement learning (RL) algorithms have been demonstrated
on a range of challenging decision making and control tasks. However, these methods …
on a range of challenging decision making and control tasks. However, these methods …
An algorithmic perspective on imitation learning
As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …
complex, unstructured settings, manually programming their behavior has become …
A survey of inverse reinforcement learning: Challenges, methods and progress
Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …