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Found 8 papers, 5 papers with code
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective
Intuitively, learning from those arbitrary demonstrations can be seen as a form of imitation learning (IL).
World Model as a Graph: Learning Latent Landmarks for Planning
Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence.
Transfer Learning for Estimating Causal Effects using Neural Networks
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
We consider the problem of exploration in meta reinforcement learning.
Evolved Policy Gradients
We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.
One-Shot Imitation Learning
A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.
Third-Person Imitation Learning
A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces.
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Atari Games
on Atari 2600 Q*Bert
