# Reinforcementlearning.jjl

**URL:** <https://discourse.julialang.org/t/reinforcementlearning-jjl/113948>\
**Category:** General Usage\
**Created:** [May 7, 2024, 2:36pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948 "2024-05-07T14:36:16Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![WuSiren](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/wusiren/32/42529_2.png) [@WuSiren](https://discourse.julialang.org/u/WuSiren)\
**Post date:** [May 7, 2024, 7:02pm UTC](https://discourse.julialang.org/t/reinforcementlearning-jjl/113948/3 "2024-05-07T19:02:37Z")

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I tried this package more than one year ago and felt its then documentation was not as friendly as at least I a newbie wish. I’ve asked a question here at that time and still been waiting for the first response so far, though I’ve almost forgotten what I was doing then. Maybe the question is too trivial? 🥴

> [@How to perform an Off-Policy reinforcement learning using \`ReinforcementLearning.jl\`?](https://discourse.julialang.org/t/how-to-perform-an-off-policy-reinforcement-learning-using-reinforcementlearning-jl/99940):
>
> I am a beginner of both Julia and RL. Now I’m trying to training a Q-based tabular policy with off-policy strategy using ReinforcementLearning.jl. Let behavior\_policy be a RandomPolicy (for example) to collect experiences, I want the target\_policy as a QBasedPolicy to be trained. I wrote the following code: target\_policy = QBasedPolicy( learner = MonteCarloLearner(; approximator=TabularQApproximator( ;n\_state = length(state\_space(wrapped\_env)), n\_action = len…

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