Questions tagged [inverse-rl]

For questions related to inverse reinforcement learning (IRL), the problem of recovering the reward function from the observed behavior (or policy) of an agent. It's called IRL because it's the inverse problem of RL, i.e. the problem of finding optimal policies given the reward function.

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What are some best practices when trying to design a reward function?

Generally speaking, is there a best-practice procedure to follow when trying to define a reward function for a reinforcement-learning agent? What common pitfalls are there when defining the reward ...
1
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2answers
412 views

How can we estimate the transition model and reward function?

In reinforcement learning (RL), there are model-based and model-free algorithms. In short, model-based algorithms use a transition model (e.g. a probability distribution) and the reward function, even ...
2
votes
0answers
59 views

What is the dimensionality of these derivatives in the paper "Active Learning for Reward Estimation in Inverse Reinforcement Learning"?

I'm trying to implement in code part of the following paper: Active Learning for Reward Estimation in Inverse Reinforcement Learning. I'm specifically referring to section 2.3 of the paper. Let's ...