CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-05 19:48:04 +08:00
Code Issues Projects Releases Wiki Activity

Fixes typo and comma(s)

Browse Source
This commit is contained in:
Juan Martinez
2023-12-06 11:10:43 +00:00
committed by GitHub
parent 306d4084c2
commit 40cf7684e5
1 changed files with 21 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
units/en/unit6/quiz.mdx
View File

@@ -10,12 +10,12 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
{
text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously tagged data we give to the model during training time.",
explain: "This is the traditional bias-variance tradeoff in Machine Learning. In our specific case of Reinforcement Learning, we don't have previously tagged data, but only a reward signal.",
correct: false,
correct: false,
},
{
{
text: "The bias-variance tradeoff reflects how well the reinforcement signal reflects the true reward the agent should get from the enviromment",
explain: "",
correct: true,
correct: true,
},
]}
/>
@@ -26,23 +26,22 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
{
text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
explain: "",
correct: true,
correct: true,
},
{
{
text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
explain: "If a reward signal is biased, it means the reward signal we get differs from the real reward we should be getting from an environment",
correct: false,
correct: false,
},
,
{
{
text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
explain: "",
correct: true,
correct: true,
},
{
{
text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
explain: "If a reward signal has low variance, then it's less affected by the noise of the environment and produce similar values regardless the random elements in the environment",
correct: false,
correct: false,
},
]}
/>
@@ -54,18 +53,17 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
{
text: "It's a sampling mechanism, which means we don't consider analyze all the possible states, but a sample of those",
explain: "",
correct: true,
correct: true,
},
{
{
text: "It's very resistant to stochasticity (random elements in the trajectory)",
explain: "Monte-carlo randomly estimates everytime a sample of trajectories. However, even same trajectories can have different reward values if they contain stochastic elements",
correct: false,
correct: false,
},
,
{
{
text: "To reduce the impact of stochastic elements in Monte-Carlo, we can take `n` strategies and average them, reducing their impact impact in case of noise"
explain: "",
correct: true,
correct: true,
},
]}
/>
@@ -85,27 +83,27 @@ The idea behind Actor-Critic is that we learn two function approximations:
### Q5: Which of the following statemets are True about the Actor-Critic Method?
<Question
choices={[
{
{
text: "The Critic does not learn from the training process",
explain: "Both the Actor and the Critic function parameters are updated during training time",
correct: false,
correct: false,
},
{
text: "The Actor learns a policy function, while the Critic learns a value function",
explain: "",
correct: true,
correct: true,
},
{
{
text: "It adds resistance to stochasticity and reduces high variance",
explain: "",
correct: true,
correct: true,
},
]}
/>
### Q6: What is `Advantege` in the A2C method?
### Q6: What is `Advantage` in the A2C method?
<details>
<summary>Solution</summary>