Jun 24, 2020 · In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable ...
A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning.
This paper forms a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space, and derives a ...
In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a ...
Bibliographic details on Control-Aware Representations for Model-based Reinforcement Learning.
This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository will be continuously updated to track the frontier ...
Abstract. Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the en- vironment to all past experience, but this wastes ...
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Jun 24, 2020 · We proposed a LCE model called control-aware representation learning (CARL) that learns representations suitable for policy iteration (PI) style ...
The proposed method achieves superior generalization ability across various sim- ulated robotics and control tasks, compared to existing RL schemes. 1.
By integrating differentiable physics-based simulation and rendering, we propose a sensing-aware model-based reinforce- ment learning system called SAM-RL. As ...