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Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward
We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback. -
Binary Tree MDP
The dataset used in the paper is a binary tree MDP, where the agent must execute a sequence of L uninterrupted UP movements. The dataset is used to test the Successor... -
Random Walk dataset
The dataset used in the paper is a collection of states sampled from a Markov Decision Process (MDP) using the random walk exploration method. -
RND dataset
The dataset used in the paper is a collection of states sampled from a Markov Decision Process (MDP) using the RND exploration method. -
SMM dataset
The dataset used in the paper is a collection of states sampled from a Markov Decision Process (MDP) using the SMM exploration method. -
ChronoGEM dataset
The dataset used in the paper is a collection of states sampled from a Markov Decision Process (MDP) using the ChronoGEM exploration method. -
Markov Decision Process
The dataset used in the paper is a Markov Decision Process, where states can take values in a state space X, corresponding to a state x ∈ X, we can take an action u ∈ U,...
