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Found 3 papers, 1 papers with code
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction
In the face of difficult exploration problems in reinforcement learning, we study whether giving an agent an object-centric mapping (describing a set of items and their attributes) allow for more efficient learning.
Light-weight probing of unsupervised representations for Reinforcement Learning
Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the training of reinforcement learning (RL) algorithms.
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions
We illustrate that incorporating predictive knowledge through an $\eta\gamma$-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i. e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models.
