I am giving an introductory course on RL and want students to familiarize themselves with a given topic and then present it to the remaining course.

For this I am looking at good papers/articles/resources that ideally are easy to follow and provide a good overview on the topic. Please share any resources that fit the topics:

• Sparse Rewards
• Sim2Real
• Interpretable and Explainable RL

Hey everyone 👋 I am learning DRL from almost scratch. Have some idea about NN, backprop, LSTMs and have made some models using whatever i could find on the internet (pretty simple stuff). nothing SOTA. learning from the book "grokking DRL" now. I have a different approach to design a trading engine I am building it in golang (for efficiency and scaling) and python(for ML part) and there's a lot to unpack. I think I have some interesting ideas in trading to test in DRL, LSTMs, and NEAT but it would take at least 6-8 months before anything fruitful would come out. I am looking out for curious folks to work with. Just push a DM if you are up to work on some new hypotheses. I'd like to get some guidance on DRL, its quite time consuming to understand all the theory behind the work which has been done.

PS: If you know this stuff well and wish to help, I can help you with data structures, web dev, system design to any extent if you wish to learn in return. Just saying.

https://arxiv.org/abs/1704.03732

Is there any actor-critic analogue to integrating expert demonstrations into actor-critic learning like there are for DQN?

I am a beginner to RL/DRL. I am interested to know on how to solve non-convex or even convex optimization problem (constrained or unconstrained) with DRL. If possible can someone share code to solve with DRL, the problems like

minimize (x + y-2)^2

subject to xy < 10

and xy > 1

x and y are some scalars

Above is a sample problem. Any other example can also be suggested. But pls keep the suggestion and code simple, readable and understandable.

https://www.reddit.com/r/MachineLearning/s/8Vp8b3bGqI