# Questions tagged [reward-shaping]

For questions related to reward shaping, which is a technique where supplemental rewards are provided to make a problem easier to learn. In general, there is usually an obvious natural reward for any problem. For games, this is usually a win or loss. For financial problems, the reward is usually profit. Reward shaping augments the natural reward signal by adding additional rewards for making progress toward a good solution.

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### How would you shape a reward function if there was four quantities to optimize?

I found this article quite useful on how to shape a reward function in RL. However, the example they gave is quite simple, where the goal is to minimize only two quantities (velocity and distance). ...
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### Reward interpolation between MDPs. Will an optimal policy on both ends stay optimal inside the interval?

Say I've got two Markov Decision Processes (MDPs): $$\mathcal{M_0} = (\mathcal{S}, \mathcal{A}, P, R_0),\quad\text{and}\quad\mathcal{M}_1 = (\mathcal{S}, \mathcal{A}, P, R_1)$$ Both have the same set ...
408 views

### Why does a negative reward for every step really encourage the agent to reach the goal as quickly as possible?

If we shift the rewards by any constant (which is a type of reward shaping), the optimal state-action value function (and so optimal policy) does not change. The proof of this fact can be found here. ...
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### Are there any reliable ways of modifying the reward function to make the rewards less sparse?

If I am training an agent to try and navigate a maze as fast as possible, a simple reward would be something like \begin{align} R(\text{terminal}) &= N - \text{time}\ \ , \ \ N \gg \text{...
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### How define a reward function for a humanoid agent whose goal is to stand up from the ground?

I'm trying to teach a humanoid agent how to stand up after falling. The episode starts with the agent lying on the floor with its back touching the ground, and its goal is to stand up in the shortest ...
806 views

### How do we define the reward function for an environment?

How do you actually decide what reward value to give for each action in a given state for an environment? Is this purely experimental and down to the programmer of the environment? So, is it a ...
87 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 ...
It is known that every potential function won't alter the optimal policy . I lack of understanding why is that. The definition: $$R' = R + F,$$ with $$F = \gamma\Phi(s') - \Phi(s),$$ where, let's ...