25,269 research outputs found
Graphical Object-Centric Actor-Critic
There have recently been significant advances in the problem of unsupervised
object-centric representation learning and its application to downstream tasks.
The latest works support the argument that employing disentangled object
representations in image-based object-centric reinforcement learning tasks
facilitates policy learning. We propose a novel object-centric reinforcement
learning algorithm combining actor-critic and model-based approaches to utilize
these representations effectively. In our approach, we use a transformer
encoder to extract object representations and graph neural networks to
approximate the dynamics of an environment. The proposed method fills a
research gap in developing efficient object-centric world models for
reinforcement learning settings that can be used for environments with discrete
or continuous action spaces. Our algorithm performs better in a visually
complex 3D robotic environment and a 2D environment with compositional
structure than the state-of-the-art model-free actor-critic algorithm built
upon transformer architecture and the state-of-the-art monolithic model-based
algorithm
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
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