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Found 7 papers, 4 papers with code
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
Efficient Risk-Averse Reinforcement Learning
In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns.
Continuous Forecasting via Neural Eigen Decomposition
Neural differential equations predict the derivative of a stochastic process.
Kalman Filter Is All You Need: Optimization Works When Noise Estimation Fails
Determining the noise parameters of a Kalman Filter (KF) has been studied for decades.
The Fragility of Noise Estimation in Kalman Filter: Optimization Can Handle Model-Misspecification
The Kalman Filter (KF) parameters are traditionally determined by noise estimation, since under the KF assumptions, the state prediction errors are minimized when the parameters correspond to the noise covariance.
Detecting Rewards Deterioration in Episodic Reinforcement Learning
In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible.
Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering
The statistical power of the new testing procedure is shown to outperform alternative tests - often by orders of magnitude - for a variety of environment modifications (which cause deterioration in agent performance).
