Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Gradients are not all you need
Differentiable programming techniques are widely used in the community and are
responsible for the machine learning renaissance of the past several decades. While these …
responsible for the machine learning renaissance of the past several decades. While these …
Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning
Eco-driving is a term used to refer to a strategy for operating vehicles so as to minimize
energy consumption. Without any hardware changes, eco-driving is an effective approach to …
energy consumption. Without any hardware changes, eco-driving is an effective approach to …
Safe reinforcement learning for model-reference trajectory tracking of uncertain autonomous vehicles with model-based acceleration
Applying reinforcement learning (RL) algorithms to control systems design remains a
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
On the model-based stochastic value gradient for continuous reinforcement learning
Abstract Model-based reinforcement learning approaches add explicit domain knowledge to
agents in hopes of improving the sample-efficiency in comparison to model-free agents …
agents in hopes of improving the sample-efficiency in comparison to model-free agents …
[HTML][HTML] Analogues of mental simulation and imagination in deep learning
JB Hamrick - Current Opinion in Behavioral Sciences, 2019 - Elsevier
Highlights•There are many methods in deep learning for learning predictive models of the
world.•Such models can be leveraged to produce behavior via a number of planning …
world.•Such models can be leveraged to produce behavior via a number of planning …
[HTML][HTML] Making sense of sensory input
This paper attempts to answer a central question in unsupervised learning: what does it
mean to “make sense” of a sensory sequence? In our formalization, making sense involves …
mean to “make sense” of a sensory sequence? In our formalization, making sense involves …
Model-based reparameterization policy gradient methods: Theory and practical algorithms
Abstract ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely
adopted for continuous control tasks in robotics and computer graphics. However, recent …
adopted for continuous control tasks in robotics and computer graphics. However, recent …
Predictable mdp abstraction for unsupervised model-based rl
A key component of model-based reinforcement learning (RL) is a dynamics model that
predicts the outcomes of actions. Errors in this predictive model can degrade the …
predicts the outcomes of actions. Errors in this predictive model can degrade the …