Topic: e-greedy Goto Github
Some thing interesting about e-greedy
Some thing interesting about e-greedy
e-greedy,Implementation of Multi-Armed Bandit (MAB) algorithms UCB and Epsilon-Greedy. MAB is a class of problems in reinforcement learning where an agent learns to choose actions from a set of arms, each associated with an unknown reward distribution. UCB and Epsilon-Greedy are popular algorithms for solving MAB problems.
User: joelja835
e-greedy,Multi Armed Bandits implementation using the Jester Dataset
User: murtazali05
e-greedy,Analysis of various multi armed bandit algorithms over normal and heavy-tailed distributions.
User: n4i9kita
e-greedy,This is a project of reinforcement learning which contains two different environments. The first environment is the taxi driver problem in 4x4 space with the simple Q-learning update rule. In this task, we compared the performance of the e-greedy policy and Boltzmann policy. As a second environment, we chose the LunarLander from the open gym. For the implementation of the project, the Policy gradient has been selected.
User: orestismk
e-greedy,
User: stepan-makarenko
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