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Found 12 papers, 1 papers with code
Generalized Preference Optimization: A Unified Approach to Offline Alignment
Offline preference optimization allows fine-tuning large models directly from offline data, and has proved effective in recent alignment practices.
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Representation learning and exploration are among the key challenges for any deep reinforcement learning agent.
Understanding Self-Predictive Learning for Reinforcement Learning
We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse.
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL) tasks.
Bootstrap your own latent: A new approach to self-supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Ranked #2 on
Self-Supervised Person Re-Identification
on SYSU-30k
Representation Learning
Self-Supervised Image Classification
+3
Directed Exploration for Reinforcement Learning
Efficient exploration is necessary to achieve good sample efficiency for reinforcement learning in general.
Neural Predictive Belief Representations
In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far.
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling.
Sample Efficient Feature Selection for Factored MDPs
This can result in a much better sample complexity when the in-degree of the necessary features is smaller than the in-degree of all features.
A PAC RL Algorithm for Episodic POMDPs
Many interesting real world domains involve reinforcement learning (RL) in partially observable environments.
