A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper solves pendulum swing-up problem using RL.
The paper introduces MDP homomorphic networks for faster reinforcement learning.
Photonic quantum reinforcement learning for control problems.
Adapts model-based advice to stabilize black-box policies for nonlinear control.
A lightweight FPGA-based reinforcement learning approach for edge devices.
A simple DQN-based multi-agent RL system for binary actions.
This paper detects Markov violations in RL with noise, improving policy development.
WAPPO optimizes feature distributions for better visual transfer in RL.
Designing optimal controllers continues to be challenging as systems are becoming complex and are inherently nonlinear. The principal advantage of reinforcement learning (RL) is its ability to learn from the interaction with the environment and provide optimal control strategy. In this paper, RL is explored in the cont…
We present a data-efficient reinforcement learning algorithm resistant to observation noise. Our method extends the highly data-efficient PILCO algorithm (Deisenroth & Rasmussen, 2011) into partially observed Markov decision processes (POMDPs) by considering the filtering process during policy evaluation. PILCO conduct…
This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier within the policy in the form of its response to a designated sequence of state transitions, while incurring minimal impact on the nominal pe…
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of consecutive policies. It comes with strong theoretical guarantees, and inspired approaches in deep Reinforcement Learning (RL). However, CPI …
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algo…
WMPG reduces policy gradient variance using world models.
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we differentiate through MPC by usin…
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpret and to impose desired specification constraints during learning. We present an iterative framework, MORL, for improving the learned polici…
Bayesian optimization has demonstrated impressive success in finding the optimum input x* and output f* = f(x*) = max f(x) of a black-box function f. In some applications, however, the optimum output f* is known in advance and the goal is to find the corresponding optimum input x*. In this paper, we consider a new sett…
D2D-SPL uses discrete states and a classifier to train RL faster.
With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention. Several implementations favor the use of Conjugate Gradient (CG) and Stochastic Gradient Descent (SGD) as being practical and elegant solutions to ac…
Quantum variational circuits improve reinforcement learning efficiency.
New STDP rule for spiking neurons solves discrete action reinforcement learning tasks.
Bayesian optimization outperforms other methods in hyperparameter tuning for reinforcement learning.
AdaRL adapts quickly to new environments with minimal data.
CARL safely adapts RL agents for safety-critical tasks.
This project proposes using reinforcement learning to train spiking neural networks.
BCPO optimizes offline RL policies by converting uncertainty into conservative bounds.
Paper introduces MVS to detect non-Markovian observations in reinforcement learning.