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Found 12 papers, 3 papers with code
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
In this work, we take a step towards bridging the gap between theory and practice by analyzing an action-conditional self-predictive objective (BYOL-AC) using the ODE framework, characterizing its convergence properties and highlighting important distinctions between the limiting solutions of the BYOL-$\Pi$ and BYOL-AC dynamics.
Offline Regularised Reinforcement Learning for Large Language Models Alignment
The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down.
Human Alignment of Large Language Models through Online Preference Optimisation
Building on this equivalence, we introduce the IPO-MD algorithm that generates data with a mixture policy (between the online and reference policy) similarly as the general Nash-MD algorithm.
Understanding plasticity in neural networks
Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems.
Hierarchical Reinforcement Learning in Complex 3D Environments
Hierarchical Reinforcement Learning (HRL) agents have the potential to demonstrate appealing capabilities such as planning and exploration with abstraction, transfer, and skill reuse.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+2
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
Neural Recursive Belief States in Multi-Agent Reinforcement Learning
We also show that higher-order belief models outperform agents with lower-order models.
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.
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
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
These latent embeddings are themselves trained to be predictive of the aforementioned representations.
