One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks.
Ranked #1 on Meta-Learning on ML10 (Meta-test success rate (zero-shot) metric)
Data efficiency is a key challenge for deep reinforcement learning.
Data efficiency poses a major challenge for deep reinforcement learning.
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
Ranked #3 on Atari Games 100k on Atari 100k
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context.
MMGAN finds two manifolds representing the vector representations of real and fake images.
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