# 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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### How to make input variable as trainable parameter in a neural network?

I am working on an optimization problem. First, I have done forward training to work the network as a surrogate model, then I freeze the output and I want to find an optimal value of input for a given ...
1answer
20 views

### Why is it that the state visitation frequency equals the sum of state visitation frequency from initial time step to the horizon?

In the maximum entropy inverse reinforcement learning paper, Ziebart et al. show that the state visitation frequency $\rho(s)$ of a state $s$ can be computed as  \rho_{\pi}(s) = \sum_{t}^{T} P(s_t=s|...
0answers
30 views

### Proving existence or non existence of reward function to make given policy “uniquely” optimal when reward function is dependent only on S or both S,A

I was going through paper titled "Algorithms for Inverse Reinforcement Learning" by Andrew Ng and Russell. It states following basics: MDP $M$ is a tuple $(S,A,\{P_{sa}\},\gamma,R)$, where ...
0answers
57 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 ...
2answers
355 views

### 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 ...
2answers
83 views

### What does the number of required expert demonstrations in Imitation Learning depend on?

I just read the following points about the number of required expert demonstrations in imitation learning, and I'd like some clarifications. For the purpose of context, I'll be using a linear reward ...
1answer
77 views

### Expressing Arbitrary Reward Functions as Potential-Based Advice (PBA)

I am trying to reproduce the results for the simple grid-world environment in . But it turns out that using a dynamically learned PBA makes the performance worse and I cannot obtain the results ...
1answer
77 views

### Can entire neural networks be composed of only activation functions?

Inverse Reinforcement Learning based on GAIL and GAN-Guided Cost Learning(GAN-GCL), uses a discriminator to classify between expert demos and policy generated samples. Adversarial iRL, build upon GAN-...
2answers
362 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 ...
1answer
116 views

### Can recovering a reward function using IRL lead to better policies compared to reward shaping?

I am working on a research project about the different reward functions being used in the RL domain. I have read up on Inverse Reinforcement Learning (IRL) and Reward Shaping (RS). I would like to ...