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Found 14 papers, 1 papers with code
Optimizing Return Distributions with Distributional Dynamic Programming
To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step).
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.
Generalized Preference Optimization: A Unified Approach to Offline Alignment
The GPO framework also sheds light on how offline algorithms enforce regularization, through the design of the convex function that defines the loss.
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
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.
