22_reinforcement_learning_from_q_value_till_the_end #24

msrazavi · 2022-01-07T18:11:54Z

SepehrAsh · 2022-01-18T07:41:25Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+Q-learning is an **off-policy** learner, which means it learns the value of the optimal policy independently of the agent’s actions. In other words, it converges to optimal policy eventually even if you are acting sub-optimally.
+Q-learning is a **sample-based** q-value iteration method and in it, you Learn $Q(s,a)$ values as you go:
+
+- Receive a sample. $(s_{t+1}, s_t, a_t, r_t)$


Please Fix the latex syntax.

SepehrAsh · 2022-01-18T07:41:41Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+
+  $$Q(s,a) \leftarrow (1 - \alpha)Q(s,a) + \alpha(sample)$$
+
+  $$\rightarrow Q^{new}(s_t,a_t) \leftarrow \underbrace{Q(s_t,a_t)}\_\text{old value} + \underbrace{\alpha}\_\text{learning rate} . \overbrace{(\underbrace{\underbrace{r_t}\_\text{reward} + \underbrace{\gamma}\_\text{discount factor} . \underbrace{\max_aQ(s_{t+1},a)}\_\text{estimate of optimal future value}}\_\text{new value (temporal difference target)} - \underbrace{Q(s_t,a_t)}\_\text{old value})}^\text{temporal difference}$$


SepehrAsh · 2022-01-18T07:42:03Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+
+<div id='epsilon-greedy-strategy'><h2> Epsilon greedy strategy </h2></div>
+
+The tradeoff between exploration and exploitation is fundamental. the simplest way to force exploration is using **epsilon greedy strategy**. This method does a random action with a small probability of $\epsilon$ (exploration) and with a probability of $(1 - \epsilon)$ does the current policy action (exploitation).


SepehrAsh · 2022-01-18T07:42:11Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+
+<div id='exploration-functions'><h2> Exploration functions </h2></div>
+
+Another solution is to use **exploration functions**. For example, this function can take a value estimate u and a visit count n, and return an optimistic utility, e.g. $f(u,n) = v + \frac{k}{n}$ . we are counting how many times we did some random action. if it had yet to reach a fixed amount, we should try it more often and if it doesn't return a good output we should just stop exploring it.


SepehrAsh · 2022-01-18T07:42:25Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+
+$$V(s) = \omega_1f_1(s) + \omega_2f_2(s) + ... + \omega_nf_n(s)$$
+
+$$Q(s,a) = \omega_1f_1(s,a) + \omega_2f_2(s,a) + ... + \omega_nf_n(s,a)$$


SepehrAsh · 2022-01-18T07:42:34Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

+
+Q-Learning is a basic form of Reinforcement Learning which uses Q-values (action values) to iteratively improve the behavior of the learning agent.
+
+Q-values are defined for states and actions. $Q(s, a)$ is an estimation of how good is it to take the action a at the state s. This estimation of $Q(s, a)$ will be iteratively computed using the temporal difference update.


SepehrAsh · 2022-01-18T07:43:24Z

notebooks/22_reinforcement_learning_from_q_value_till_the_end/index.md

@@ -0,0 +1,130 @@
+<div id='reinforcement-learning-from-q-value-till-the-end'><h1> Reinforcement Learning (from Q Value till the end) </h1></div>
+


General Note: please fix the latex syntax for formulas.

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22_reinforcement_learning_from_q_value_till_the_end #24

22_reinforcement_learning_from_q_value_till_the_end #24

msrazavi commented Jan 7, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022

SepehrAsh Jan 18, 2022


		$$Q(s,a) \leftarrow (1 - \alpha)Q(s,a) + \alpha(sample)$$

		$$\rightarrow Q^{new}(s_t,a_t) \leftarrow \underbrace{Q(s_t,a_t)}\_\text{old value} + \underbrace{\alpha}\_\text{learning rate} . \overbrace{(\underbrace{\underbrace{r_t}\_\text{reward} + \underbrace{\gamma}\_\text{discount factor} . \underbrace{\max_aQ(s_{t+1},a)}\_\text{estimate of optimal future value}}\_\text{new value (temporal difference target)} - \underbrace{Q(s_t,a_t)}\_\text{old value})}^\text{temporal difference}$$


		<div id='epsilon-greedy-strategy'><h2> Epsilon greedy strategy </h2></div>

		The tradeoff between exploration and exploitation is fundamental. the simplest way to force exploration is using epsilon greedy strategy. This method does a random action with a small probability of $\epsilon$ (exploration) and with a probability of $(1 - \epsilon)$ does the current policy action (exploitation).


		<div id='exploration-functions'><h2> Exploration functions </h2></div>

		Another solution is to use exploration functions. For example, this function can take a value estimate u and a visit count n, and return an optimistic utility, e.g. $f(u,n) = v + \frac{k}{n}$ . we are counting how many times we did some random action. if it had yet to reach a fixed amount, we should try it more often and if it doesn't return a good output we should just stop exploring it.


		$$V(s) = \omega_1f_1(s) + \omega_2f_2(s) + ... + \omega_nf_n(s)$$

		$$Q(s,a) = \omega_1f_1(s,a) + \omega_2f_2(s,a) + ... + \omega_nf_n(s,a)$$


		Q-Learning is a basic form of Reinforcement Learning which uses Q-values (action values) to iteratively improve the behavior of the learning agent.

		Q-values are defined for states and actions. $Q(s, a)$ is an estimation of how good is it to take the action a at the state s. This estimation of $Q(s, a)$ will be iteratively computed using the temporal difference update.

		@@ -0,0 +1,130 @@
		<div id='reinforcement-learning-from-q-value-till-the-end'><h1> Reinforcement Learning (from Q Value till the end) </h1></div>

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