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Found 6 papers, 2 papers with code
PcLast: Discovering Plannable Continuous Latent States
Goal-conditioned planning benefits from learned low-dimensional representations of rich, high-dimensional observations.
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making.
Tsetlin Machine for Solving Contextual Bandit Problems
This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic.
Approximate information state for approximate planning and reinforcement learning in partially observed systems
Our key result is to show that if a function of the history (called approximate information state (AIS)) approximately satisfies the properties of the information state, then there is a corresponding approximate dynamic program.
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution.
