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Found 28 papers, 10 papers with code
Fast computation of Nash Equilibria in Imperfect Information Games
We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.
A General Theoretical Paradigm to Understand Learning from Human Preferences
In particular we derive a new general objective called $\Psi$PO for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations.
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performing practical RL algorithms.
An Analysis of Quantile Temporal-Difference Learning
We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning.
Distributional Reinforcement Learning
reinforcement-learning
+1
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.
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model.
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).
Bootstrapped Representation Learning on Graphs
Current state-of-the-art self-supervised learning methods for graph neural networks are based on contrastive learning.
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample.
Large-Scale Representation Learning on Graphs via Bootstrapping
To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.
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.
The Advantage Regret-Matching Actor-Critic
In this paper, we describe a general model-free RL method for no-regret learning based on repeated reconsideration of past behavior.
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
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
These latent embeddings are themselves trained to be predictive of the aforementioned representations.
Towards Consistent Performance on Atari using Expert Demonstrations
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
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.
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration.
Ranked #1 on
Atari Games
on Atari 2600 Surround
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting.
Ranked #6 on
Atari Games
on Atari 2600 Crazy Climber
Minimax Regret Bounds for Reinforcement Learning
We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs.
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes
Our approach enables the use of convex optimization tools to solve a class of non-convex optimization problems.
Online Stochastic Optimization under Correlated Bandit Feedback
In this paper we consider the problem of online stochastic optimization of a locally smooth function under bandit feedback.
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents.
Regret Bounds for Reinforcement Learning with Policy Advice
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors.
