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Found 6 papers, 0 papers with code
Value-aware Importance Weighting for Off-policy Reinforcement Learning
Importance sampling is a central idea underlying off-policy prediction in reinforcement learning.
Incremental Policy Gradients for Online Reinforcement Learning Control
Our emphasis is on the first approach in this work, detailing an incremental policy gradient update which neither waits until the end of the episode, nor relies on learning estimates of the return.
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed}$ number of future time steps.
Predicting Periodicity with Temporal Difference Learning
Temporal difference (TD) learning is an important approach in reinforcement learning, as it combines ideas from dynamic programming and Monte Carlo methods in a way that allows for online and incremental model-free learning.
Per-decision Multi-step Temporal Difference Learning with Control Variates
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme.
Multi-step Reinforcement Learning: A Unifying Algorithm
These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance.
