# Questions tagged [discount-factor]

Use for question involving discount factor (γ) in reinforcement learning.

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### Understanding the On-policy state distribution for episodic tasks with $\gamma \in (0,1)$

In Sutton and Barto's Reinforcement Learning: An Introduction, section 9.2 (page 199) (here is a screenshot) describes the on-policy distribution in episodic tasks, with $\gamma =1$, as being \begin{...
502 views

### Is the discount not needed in a deterministic environment for Reinforcement Learning?

I'm now reading a book titled as "Deep Reinforcement Learning Hands-On" and the author said the following on the chapter about AlphaGo Zero: Self-play In AlphaGo Zero, the NN is used to ...
726 views

### Can the optimal value of discount factor in Deep Reinforcement Learning be between 0.2 to 0.8?

I'm now reading a book titled as Hands-On Reinforcement Learning with Python, and the author explains the discount factor that is used in Reinforcement Learing to discount the future reward, with the ...
156 views

### What is the value of a state when there is a certain probability that agent will die after each step?

We assume infinite horizon and discount factor $\gamma = 1$. At each step, after the agent takes an action and gets its reward, there is a probability $\alpha = 0.2$, that agent will die. The assumed ...
97 views

### Are there any discount-factors based on branching factors?

I recently came across this function: $$\sum_{t = 0}^{\infty} \gamma^t R_t.$$ It's elegant and looks to be useful in the type of deterministic, perfect-information, finite models I'm working with. ...
41 views

### Does everyone still use discount rates?

In Section 10.4 of Sutton and Barto's RL book, they argue that the discount rate $\gamma$ has no effect in continuing settings. They show (at least for one objective function) that the average of the ...
108 views

### Updating action-value functions in Semi-Markov Decision Process and Reinforcement Learning

Suppose that the transition time between two states is a random variable (for example, unknown exponential distribution); and between two arrivals, there is no reward. If $\tau$ (real number not an ...
485 views

### In online one step actor critic, why does the weights update become less significant as the episode progresses?

The Reinforcement Learning Book by Richard Sutton et al, section 13.5 shows an online actor critic algorithm. Why do the weights updates depend on the discount factor via $I$? It seems that the more ...
37 views

### Combine DQN with the Average Reward setting

I have to deal with a non-episodic task, where there is addittionally a continuous state space and more specifically in each time step there is always a new state that has never been seen before. I ...
43 views

### For episodic tasks with an absorbing state, why can't we both have $\gamma=1$ and $T= \infty$ in the definition of the return?

For episodic tasks with an absorbing state, why can't $\gamma=1$ and $T= \infty$? In Sutton and Barto's book, they say that, for episodic tasks with absorbing states that becomes an infinite sequence, ...
153 views

### How do I calculate the return given the discount factor and a sequence of rewards?

I know that $G_t = R_{t+1} + G_{t+1}$. Suppose $\gamma = 0.9$ and the reward sequence is $R_1 = 2$ followed by an infinite sequence of $7$s. What is the value of $G_0$? As it's infinite, how can we ...