• Corpus ID: 209386673

Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game

@article{Weihs2019ArtificialAL,
  title={Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game},
  author={Luca Weihs and Aniruddha Kembhavi and Kiana Ehsani and Sarah Pratt and Winson Han and Alvaro Herrasti and Eric Kolve and Dustin Schwenk and Roozbeh Mottaghi and Ali Farhadi},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.08195}
}
The ubiquity of embodied gameplay, observed in a wide variety of animal species including turtles and ravens, has led researchers to question what advantages play provides to the animals engaged in it. Mounting evidence suggests that play is critical in developing the neural flexibility for creative problem solving, socialization, and can improve the plasticity of the medial prefrontal cortex. Comparatively little is known regarding the impact of gameplay upon embodied artificial agents. While… 

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References

SHOWING 1-10 OF 56 REFERENCES

Human-level control through deep reinforcement learning

TLDR
This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Visual Hide and Seek

TLDR
This work trains embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator and quantitatively analyzes how agent weaknesses, such as slower speed, effect the learned policy.

Children and robots learning to play hide and seek

TLDR
It is proposed that children are able to learn how to play hide and seek by learning the features and relations of objects and use that information to play a credible game ofHide and seek.

Mastering the game of Go without human knowledge

TLDR
An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

Curiosity-Driven Exploration by Self-Supervised Prediction

TLDR
This work forms curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model, which scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and ignores the aspects of the environment that cannot affect the agent.

Hindsight Experience Replay

TLDR
A novel technique is presented which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering and may be seen as a form of implicit curriculum.

The Developmental Progression of Understanding of Mind during a Hiding Game.

TLDR
Observing preschoolers engaged in a playful hiding game revealed that children's understanding of mind not only increased with age but also developed sequentially, which suggests that mothers may tailor the content of their utterances to the child's growing expertise.

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

TLDR
This paper generalises the approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains, and convincingly defeated a world-champion program in each case.

Games and the Development of Perspective Taking

It is widely acknowledged that perspective taking is fundamental to the development of the self, the development of the individual’s ability to interact meaningfully with other people, and to the
...