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Found 11 papers, 4 papers with code
Spectral Temporal Contrastive Learning
This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts.
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning
Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and utilizing a notion of a guarantee or confidence about the behavior of the system.
Discovering Object-Centric Generalized Value Functions From Pixels
Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards.
Locally Constrained Representations in Reinforcement Learning
This encourages the representations to be driven not only by the value/policy learning but also by an additional loss that constrains the representations from over-fitting to the value loss.
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management
Through empirical evaluations, it is further shown that the inventory management with uncertain lead times is not only equivalent to that of delay in information sharing across multiple echelons (\emph{observation delay}), a model trained to handle one kind of delay is capable to handle delays of another kind without requiring to be retrained.
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Exploration methods have been used to sample better trajectories in large environments while auxiliary tasks have been incorporated where the reward is sparse.
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays
Several real-world scenarios, such as remote control and sensing, are comprised of action and observation delays.
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains.
Training Recurrent Neural Networks Online by Learning Explicit State Variables
Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in partially observable problems.
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning
We propose a generic reward shaping approach for improving the rate of convergence in reinforcement learning (RL), called Self Improvement Based REwards, or SIBRE.
Two-Timescale Networks for Nonlinear Value Function Approximation
A key component for many reinforcement learning agents is to learn a value function, either for policy evaluation or control.
