8 votes
Accepted

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

Designing reward functions Designing a reward function is sometimes straightforward, if you have knowledge of the problem. For example, consider the game of chess. You know that you have three ...
nbro's user avatar
  • 40.2k
6 votes

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

If your objective is for the agent to attain some goal (say, reaching a target), then a valid reward function is to assign a reward of 1 when the goal is attained and 0 otherwise. The problem with ...
user76284's user avatar
  • 347
3 votes
Accepted

Reward design or Inverse reinforcement learning?

It depends on the domain you are in. Inverse RL (IRL) would be most advantageous in domains in which: It's hard to specify the reward by hand: for example, it would be hard to hand-specify a reward ...
mdc's user avatar
  • 380
2 votes

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

Inverse Reinforcement Learning (IRL) is a technique that attempts to recover the reward function that the expert is implicitly maximising based on expert demonstrations. When solving reinforcement ...
calveeen's user avatar
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2 votes
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Expressing Arbitrary Reward Functions as Potential-Based Advice (PBA)

Is the method itself defective or anything wrong with my code? There does indeed appear to be an issue with the code, the publications are fine (I know most of those authors and would very much trust ...
Dennis Soemers's user avatar
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1 vote

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

The equation you show does not appear in Ziebart et al (2008). They do provide a description of the computation in Algorithm 1. It is the visitation frequency and it is not a probability distribution, ...
Tessa van der Heiden's user avatar
1 vote

Can entire neural networks be composed of only activation functions?

The Pytorch docs define a fully connected ReLU network as: torch.nn.Sequential( torch.nn.Linear(D_in, H), torch.nn.ReLU(), torch.nn.Linear(H, D_out), ) ...
mugoh's user avatar
  • 531
1 vote
Accepted

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

The answer to your question can be found in the original paper that introduced the max-margin and projection imitation learning (IL) algorithms: Apprenticeship Learning via Inverse Reinforcement ...
nbro's user avatar
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