Paper notes
Action-Conditional Video Prediction using Deep Networks in Atari Games
First arXiv submission: · NeurIPS 2015
Learns to predict Atari frames from past observations and control inputs, connecting visual generation to an agent's actions.
Contribution and setting
Develops feedforward and recurrent architectures with action-dependent transformations and evaluates long visual rollouts and their usefulness for control.
- Model output
- Predicted game frames.
- Action interface
- Discrete Atari control actions.
- State and memory
- Past observations; the recurrent variant carries a hidden state through time.
Relationship to NeuralOS
Earlier action-conditioned visual-prediction work cited in NeuralOS's introduction and references.
Scope and limitations
The experiments use Atari games.
Related terms: action-conditioned video prediction, visual dynamics, Atari, learned simulator.
Cite this paper
ArXiv preprint
arXiv:1507.08750, initial submission 2015-07-31.