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Found 6 papers, 3 papers with code
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
Composing Entropic Policies using Divergence Correction
Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning.
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring $100,\! 000$s of time steps and memories.
Ranked #6 on
Question Answering
on bAbi
(Mean Error Rate metric)
Successor Features for Transfer in Reinforcement Learning
Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks.
Memory-based control with recurrent neural networks
Partially observed control problems are a challenging aspect of reinforcement learning.
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
Ranked #3 on
Continuous Control
on Lunar Lander (OpenAI Gym)
