DRL | A collection of Deep Reinforcement Learning algorithms | Machine Learning library

You are looking for Vectorized Environments. They will allow parallel interaction with your environments.

There was nothing wrong with the network definition. It turns out the learning rate was too high and reducing it 0.00025 (as in the original Nature paper introducing the DQN) led to an agent which can solve CartPole-v0.

That said, the learning algorithm was incorrect. In particular I was using the wrong target action-value predictions. Note the algorithm laid out above does not use the most recent version of the target network to make predictions. This leads to poor results as training progresses because the agent is learning based on stale target data. The way to fix this is to just put (s, a, r, s', done) into the replay memory and then make target predictions using the most up to date version of the target network when sampling a mini batch. See the code below for an updated learning loop.

This is a bug in Optaplanner, the current workaround is to move the needed code from the different modules into one. Not ideal, but it works. To follow the issue : https://issues.redhat.com/browse/PLANNER-2600 Once resolved, this bug should not happen anymore.

One way in which you could do this would be to generate a row for each criteria and then aggregate up those that return a match, which can be done using cross apply and a values table generator that ensures all your case expressions are evaluated for all wells.

This approach at present assumes that each well only has the one WellCategory value. If there can be more than one then you will need to make those changes yourself. I also haven't wrapped the output in a pivot as I think this is generally something best done in your presentation layer rather than the raw SQL.

I suggest keeping the SQL output in this format as presentation tools are much better at dynamically handling new categories (e.g. a new LeaseType is added) than SQL is, which in this normalised format wouldn't require a change in either the script or the presentation layer, barring any bespoke labels.

If you want to retain the pivoted output, you can simply wrap this query in your current outer select:

Congratulation, you found drools bug DROOLS-6542 - fixed in 7.60.0.Final
There is a workaround - remove mvel dialect for the rule "Invalidate visa application with invalid passport".

BTW, I'd like to propose you drools testing library which may save you great amount of time to mind complicated rules and simplify writing test scenarios. Here is how test may look like.

As you said, you are inserting the lists, but not the individual objects as facts. Your rules are written for individual facts. So you either insert each element inside your lists in the session, or you create a rule that does that for you:

I'm going to assume all of these variables exist inside of an class which I'm going to call Inputs. You'd call these rules by passing an instance of Inputs into the rules.

I'm also going to assume that the last 'else if' in your example was a type and you're actually checking that variableN == 2.

There was a non-backwards-compatible change to the Drools library introduced in 7.45. You can read about it in the documentation's release notes here.

Basically, starting in 7.45, the introduction of "executable models" caused mvel support to be broken out into a separate module. As a result you now need to include one of the following dependencies:

• drools-mvel-compiler
• drools-model-compiler

Which one you need depends on what you're actually doing. Prior to 7.45, the mvel compiler support was part of the drools-compiler dependency, so this extra dependency was not necessary.

(As a note, you don't need the kie-api dependency.)

You can't reference $exportData there.

You could, as a workaround, move the parent salary logic outside of the Child reference like this: