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Found 11 papers, 3 papers with code
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e. g., stochastic gradient descent and temporal difference learning.
IDDR-NGP: Incorporating Detectors for Distractors Removal with Instant Neural Radiance Field
To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes.
GRID: A Platform for General Robot Intelligence Development
In addition, the modular design enables various deep ML components and existing foundation models to be easily usable in a wider variety of robot-centric problems.
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning.
PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise and prior knowledge.
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain.
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface
In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
This is motivation for the focus on mean square error bounds for parameter estimates.
Zap Q-Learning With Nonlinear Function Approximation
Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.
Zap Q-Learning for Optimal Stopping Time Problems
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$.
