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Found 3 papers, 1 papers with code
On learning history based policies for controlling Markov decision processes
Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods, suchas recurrent neural nets or history-based state abstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP.
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging.
Variance Penalized On-Policy and Off-Policy Actor-Critic
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent.
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