```
import gym
import matplotlib
import numpy as np
import sys
from collections import defaultdict
import pprint as pp
from matplotlib import pyplot as plt
%matplotlib inline
```

## 1. Introduction

In a *Markov Decision Process* (Figure 1) the *agent* and *environment* interacts continuously.

More details are available in Reinforcement Learning: An Introduction by Sutton and Barto.

The dynamics of the MDP is given by \[ \begin{aligned} p(s',r|s,a) &= Pr\{ S_{t+1}=s',R_{t+1}=r | S_t=s,A_t=a \} \\ \end{aligned} \]

The *policy* of an agent is a mapping from the current state of the environment to an *action* that the agent needs to take in this state. Formally, a policy is given by \[
\begin{aligned}
\pi(a|s) &= Pr\{A_t=a|S_t=s\}
\end{aligned}
\]

The discounted *return* is given by \[
\begin{aligned}
G_t &= R_{t+1} + \gamma R_{t+2} + \gamma ^2 R_{t+3} + ... + R_T \\
&= \sum_{k=0}^\infty \gamma ^k R_{t+1+k}
\end{aligned}
\] where \(\gamma\) is the discount factor and \(R\) is the *reward*.

Most reinforcement learning algorithms involve the estimation of value functions - in our present case, the *state-value function*. The state-value function maps each state to a measure of “how good it is to be in that state” in terms of expected rewards. Formally, the state-value function, under policy \(\pi\) is given by \[
\begin{aligned}
v_\pi(s) &= \mathbb{E}_\pi[G_t|S_t=s]
\end{aligned}
\]

The Monte Carlo algorithm discussed in this post will numerically estimate \(v_\pi(s)\).

## 2. Environment

The environment is the game of *Blackjack*. The player tries to get cards whose sum is as great as possible without exceeding 21. Face cards count as 10. An ace can be taken either as a 1 or an 11. Two cards are dealth to both dealer and player. One of the dealer’s cards is face up (other is face down). The player can request additional cards, one by one (called *hits*) until the player stops (called *sticks*) or goes above 21 (goes *bust* and loses). When the players sticks it becomes the dealer’s turn which uses a fixed strategy: sticks when the sum is 17 or greater and hits otherwise. If the dealer goes bust the player wins, otherwise the winner is determined by whose sum is closer to 21.

We formulate this game as an episodic finite MDP. Each game is an episode.

- States are based on the player’s
- current sum (12-21)
- player will automatically keep on getting cards until the sum is at least 12 (this is a rule and the player does not have a choice in this matter)

- dealer’s face up card (ace-10)
- whether player holds usable ace (True or False)

- current sum (12-21)

This gives a total of 200 states: \(10 × 10 \times 2 = 200\)

- Rewards:
- +1 for winning
- -1 for losing
- 0 for drawing

- Reward for stick:
- +1 if sum > sum of dealer
- 0 if sum = sum of dealer
- -1 if sum < sum of dealer

- Reward for hit:
- -1 if sum > 21
- 0 otherwise

The environment is implemented using the OpenAI Gym library.

## 3. Agent

The *agent* is the player. After observing the state of the *environment*, the agent can take one of two possible actions:

- stick (0) [stop receiving cards]
- hit (1) [have another card]

The agent’s policy will be deterministic - will always stick of the sum is 20 or 21, and hit otherwise. We call this *policy1* in the code.

## 4. Monte Carlo Estimation of the Action-value Function, \(q_\pi(s,a)\)

We will now proceed to estimate the action-value function for the given policy \(\pi\). We can take \(\gamma=1\) as the sum will remain finite:

\[ \large \begin{aligned} q_\pi(s,a) &= \mathbb{E}_\pi[G_t | S_t=s, A_t=a] \\ &= \mathbb{E}_\pi[R_{t+1} + \gamma R_{t+2} + \gamma ^2 R_{t+3} + ... + R_T | S_t=s, A_t=a] \\ &= \mathbb{E}_\pi[R_{t+1} + R_{t+2} + R_{t+3} + ... + R_T | S_t=s, A_t=a] \end{aligned} \]

In numeric terms this means that, given a state and an action, we take the sum of all rewards from that state onwards (following policy \(\pi\)) until the game ends, and take the average of all such sequences.

## 5. Implementation

We consider two versions of the MC Prediction algorithm: A forward version, and a backward version.

Figure 2 shows the forward version:

Figure 3 shows the backward version:

Next, we present the code that implements the algorithm.

```
# hide
# from lib import plotting as myplot
# from lib.envs.blackjack import BlackjackEnv
from dennybritz_lib import plotting as myplot
from dennybritz_lib.envs.blackjack import BlackjackEnv
```

`= BlackjackEnv() env `

### 5.1 Policy

The following function captures the policy used by the agent:

```
def policy1(observation):
= observation
player_sum, dealer_showing, usable_ace if player_sum >=20:
return 0 #stick
else:
return 1 #hit
```

### 5.2 Generate episodes

The following function sets the environment to a random initial state. It then enters a loop where each iteration applies the policy to the environment’s state to obtain the next action to be taken by the agent. That action is then applied to the environment to get the next state, and so on until the episode ends.

```
def generate_episode(env, policy):
= []
episode = env.reset() #to a random state
state = np.random.choice([0, 1]) #to a random action, for exploring starts
action while True:
# action = policy(state)
= env.step(action) # St+1, Rt+1 OR s',r
next_state, reward, done, _ # St, At, Rt+1 OR s,a,r
episode.append((state, action, reward)) if done:
break
= next_state
state = policy(state)
action return episode
```

### 5.3 Main loop

The following function implements the main loop of the algorithm. It iterates for `n_episodes`

. It also takes a list of `monitored_state_actions`

for which it will record the evolution of action values. This is handy for showing how action values converge during the process.

#### 5.3.1 First-Visit Forward Algorithm

```
def first_visit_forward_algorithm(episode, G_sum, G_cnt, Q, discount_factor, diag):
# Find all visited state-actions in this episode
= set([(tuple(sar[0]), sar[1]) for sar in episode]); print(f'-episode_state_actions: {episode_state_actions}') if diag else None
episode_state_actions for state,action in episode_state_actions: #don't use St,At, they come from set, time seq not relevant
= (state, action)
state_action #--find the first visit to the state_action in the episode
# first_visit_ix = next(i for i,sar in enumerate(episode) if sar[0]==state and sar[1]==action)
= [i for i,sar in enumerate(episode) if sar[0]==state and sar[1]==action]; print(f'---state_action {state_action} visit_ixs: {visit_ixs}') if diag else None
visit_ixs = visit_ixs[0]; print(f"first_visit_ix: {first_visit_ix}") if diag else None
first_visit_ix #--sum up all rewards since the first visit
print(f"episode[first_visit_ix:]: {episode[first_visit_ix:]}") if diag else None
print(f"rewards: {[sar[2]*(discount_factor**i) for i,sar in enumerate(episode[first_visit_ix:])]}") if diag else None
= sum([sar[2]*(discount_factor**i) for i,sar in enumerate(episode[first_visit_ix:])]); print(f"G: {G}") if diag else None
G #--average return for this state_action over all sampled episodes
#--instead of appending, keep a running sum and count
+= G; G_cnt[state_action] += 1.0
G_sum[state_action] = G_sum[state_action]/G_cnt[state_action] Q[state][action]
```

#### 5.3.2 First-Visit Backward Algorithm

```
def first_visit_backward_algorithm(episode, G_sum, G_cnt, Q, discount_factor, diag):
= 0.0
G = [(sar[0], sar[1]) for sar in episode]; print(f'-episode_state_actions: {episode_state_actions}') if diag else None #put St,At in tuple and use as key
episode_state_actions for t in range(len(episode))[::-1]:
= episode[t]
St, At, Rtp1 print(f"---t={t} St, At, Rt+1: {St, At, Rtp1}") if diag else None
= discount_factor*G + Rtp1
G print(f"G: {G}") if diag else None
if (St,At) not in episode_state_actions[0:t]: #S0,A0,S1,A1,...,St-1,At-1, i.e. all earlier states
print(f"{(St,At)} not in {episode_state_actions[0:t]}, processing ...") if diag else None
+= G; print(f"G_sum[(St,At)]: {G_sum[(St,At)]}") if diag else None
G_sum[(St,At)] += 1.0; print(f"G_cnt[(St,At)]: {G_cnt[(St,At)]}") if diag else None
G_cnt[(St,At)] = G_sum[(St,At)]/G_cnt[(St,At)]; print(f"Q[St][At]: {Q[St][At]}") if diag else None
Q[St][At] else:
print(f"{(St,At)} IS in {episode_state_actions[0:t]}, skipping ...") if diag else None
```

#### 5.3.3 Final First-Visit Algorithm

We decide on using the *backward* version from now on. It may be a bit more challenging to understand, but it provides more efficient computation. In the next function, we always call the backward version by means of the call:

`first_visit_forward_algorithm(episode, G_sum, G_cnt, Q, discount_factor, diag)`

```
def mc_estimation(env, n_episodes, discount_factor=1.0, monitored_state_actions=None, diag=False):
= defaultdict(float)
G_sum = defaultdict(float)
G_count = defaultdict(lambda: np.zeros(env.action_space.n)) #final action-value function
Q = policy1
pi = defaultdict(list)
monitored_state_action_values for i in range(1, n_episodes + 1):
if i%1000 == 0: print("\rEpisode {}/{}".format(i, n_episodes), end=""); sys.stdout.flush()
= generate_episode(env, pi); print(f'\nepisode {i}: {episode}') if diag else None
episode # first_visit_forward_algorithm(episode, G_sum, G_cnt, Q, discount_factor, diag)
first_visit_backward_algorithm(episode, G_sum, G_cnt, Q, discount_factor, diag)if monitored_state_actions:
for msa in monitored_state_actions:
= msa[0]; a = msa[1]
s # print("\rQ[{}]: {}".format(msa, Q[s][a]), end=""); sys.stdout.flush()
monitored_state_action_values[msa].append(Q[s][a])print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++') if diag else None
f'G_sum: {G_sum}') if diag else None
pp.pprint(f'G_count: {G_count}') if diag else None
pp.pprint(print('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++') if diag else None
print('\nmonitored_state_action_values:', monitored_state_action_values) if diag else None
return Q,monitored_state_action_values
```

### 5.4 Monitored state-actions

Let’s pick a number of state-actions to monitor. Each tuple captures the player’s sum, the dealer’s showing card, and whether the player has a usable ace, as well as the action taken in the state:

`=[((21, 7, False), 0), ((20, 7, True), 0), ((12, 7, False), 1), ((17, 7, True), 0)] monitored_state_actions`

```
= mc_estimation(
Q,monitored_state_action_values
env, =10,
n_episodes=monitored_state_actions,
monitored_state_actions=True) diag
```

```
episode 1: [((13, 7, False), 1, 0), ((19, 7, False), 1, 0), ((21, 7, False), 0, 1)]
-episode_state_actions: [((13, 7, False), 1), ((19, 7, False), 1), ((21, 7, False), 0)]
---t=2 St, At, Rt+1: ((21, 7, False), 0, 1)
G: 1.0
((21, 7, False), 0) not in [((13, 7, False), 1), ((19, 7, False), 1)], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
---t=1 St, At, Rt+1: ((19, 7, False), 1, 0)
G: 1.0
((19, 7, False), 1) not in [((13, 7, False), 1)], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
---t=0 St, At, Rt+1: ((13, 7, False), 1, 0)
G: 1.0
((13, 7, False), 1) not in [], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
episode 2: [((13, 4, False), 0, -1)]
-episode_state_actions: [((13, 4, False), 0)]
---t=0 St, At, Rt+1: ((13, 4, False), 0, -1)
G: -1.0
((13, 4, False), 0) not in [], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
episode 3: [((14, 5, False), 0, -1)]
-episode_state_actions: [((14, 5, False), 0)]
---t=0 St, At, Rt+1: ((14, 5, False), 0, -1)
G: -1.0
((14, 5, False), 0) not in [], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
episode 4: [((13, 3, False), 1, -1)]
-episode_state_actions: [((13, 3, False), 1)]
---t=0 St, At, Rt+1: ((13, 3, False), 1, -1)
G: -1.0
((13, 3, False), 1) not in [], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
episode 5: [((16, 8, False), 0, 1)]
-episode_state_actions: [((16, 8, False), 0)]
---t=0 St, At, Rt+1: ((16, 8, False), 0, 1)
G: 1.0
((16, 8, False), 0) not in [], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
episode 6: [((20, 10, False), 0, 1)]
-episode_state_actions: [((20, 10, False), 0)]
---t=0 St, At, Rt+1: ((20, 10, False), 0, 1)
G: 1.0
((20, 10, False), 0) not in [], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
episode 7: [((20, 10, False), 1, -1)]
-episode_state_actions: [((20, 10, False), 1)]
---t=0 St, At, Rt+1: ((20, 10, False), 1, -1)
G: -1.0
((20, 10, False), 1) not in [], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
episode 8: [((17, 10, False), 0, 1)]
-episode_state_actions: [((17, 10, False), 0)]
---t=0 St, At, Rt+1: ((17, 10, False), 0, 1)
G: 1.0
((17, 10, False), 0) not in [], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: 1.0
episode 9: [((20, 10, False), 0, 0)]
-episode_state_actions: [((20, 10, False), 0)]
---t=0 St, At, Rt+1: ((20, 10, False), 0, 0)
G: 0.0
((20, 10, False), 0) not in [], processing ...
G_sum[(St,At)]: 1.0
G_cnt[(St,At)]: 2.0
Q[St][At]: 0.5
episode 10: [((17, 10, False), 1, 0), ((19, 10, False), 1, -1)]
-episode_state_actions: [((17, 10, False), 1), ((19, 10, False), 1)]
---t=1 St, At, Rt+1: ((19, 10, False), 1, -1)
G: -1.0
((19, 10, False), 1) not in [((17, 10, False), 1)], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
---t=0 St, At, Rt+1: ((17, 10, False), 1, 0)
G: -1.0
((17, 10, False), 1) not in [], processing ...
G_sum[(St,At)]: -1.0
G_cnt[(St,At)]: 1.0
Q[St][At]: -1.0
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
("G_sum: defaultdict(<class 'float'>, {((21, 7, False), 0): 1.0, ((19, 7, "
'False), 1): 1.0, ((13, 7, False), 1): 1.0, ((13, 4, False), 0): -1.0, ((14, '
'5, False), 0): -1.0, ((13, 3, False), 1): -1.0, ((16, 8, False), 0): 1.0, '
'((20, 10, False), 0): 1.0, ((20, 10, False), 1): -1.0, ((17, 10, False), 0): '
'1.0, ((19, 10, False), 1): -1.0, ((17, 10, False), 1): -1.0})')
"G_count: defaultdict(<class 'float'>, {})"
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
monitored_state_action_values: defaultdict(<class 'list'>, {((21, 7, False), 0): [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ((20, 7, True), 0): [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ((12, 7, False), 1): [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ((17, 7, True), 0): [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]})
```

` Q`

```
defaultdict(<function __main__.mc_estimation.<locals>.<lambda>>,
{(12, 7, False): array([0., 0.]),
(13, 3, False): array([ 0., -1.]),
(13, 4, False): array([-1., 0.]),
(13, 7, False): array([0., 1.]),
(14, 5, False): array([-1., 0.]),
(16, 8, False): array([1., 0.]),
(17, 7, True): array([0., 0.]),
(17, 10, False): array([ 1., -1.]),
(19, 7, False): array([0., 1.]),
(19, 10, False): array([ 0., -1.]),
(20, 7, True): array([0., 0.]),
(20, 10, False): array([ 0.5, -1. ]),
(21, 7, False): array([1., 0.])})
```

`13, 5, False)] Q[(`

`array([0., 0.])`

`13, 5, False)][0], Q[(13, 5, False)][1] Q[(`

`(0.0, 0.0)`

```
print(monitored_state_actions[0])
print(monitored_state_action_values[monitored_state_actions[0]])
```

```
((21, 7, False), 0)
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
```

```
# last value in monitored_state_actions should be value in Q
= monitored_state_actions[0]; print('msa:', msa)
msa = msa[0]; print('s:', s)
s = msa[1]; print('a:', a)
a -1], Q[s][a] #monitored_stuff[msa] BUT Q[s][a] monitored_state_action_values[msa][
```

```
msa: ((21, 7, False), 0)
s: (21, 7, False)
a: 0
```

`(1.0, 1.0)`

### 5.5 Run 1

First, we will run the algorithm for 10,000 episodes, using policy1:

```
= mc_estimation(
Q1,monitored_state_action_values1
env, =10_000,
n_episodes=monitored_state_actions,
monitored_state_actions=False) diag
```

`Episode 10000/10000`

```
# last value in monitored_state_actions should be value in Q
= monitored_state_actions[0]; print('msa:', msa)
msa = msa[0]; print('s:', s)
s = msa[1]; print('a:', a)
a -1], Q1[s][a] #monitored_stuff[msa] BUT Q[s][a] monitored_state_action_values1[msa][
```

```
msa: ((21, 7, False), 0)
s: (21, 7, False)
a: 0
```

`(0.925, 0.925)`

The following chart shows how the values of the 4 monitored state-actions converge to their values:

```
"figure.figsize"] = (18,10)
plt.rcParams[for msa in monitored_state_actions:
plt.plot(monitored_state_action_values1[msa])'Estimated $q_\pi(s,a)$ for some state-actions', fontsize=18)
plt.title('Episodes', fontsize=16)
plt.xlabel('Estimated $q_\pi(s,a)$', fontsize=16)
plt.ylabel(=16)
plt.legend(monitored_state_actions, fontsize plt.show()
```

The following wireframe charts shows the estimate of the action-value function, \(q_\pi(s,a)\), for the cases of a usable ace as well as not a usable ace:

```
= -110
AZIM = 20 ELEV
```

`="10,000 Steps", wireframe=True, azim=AZIM, elev=ELEV) myplot.plot_action_value_function(Q1, title`

Here are the same charts but with coloration:

`="10,000 Steps", wireframe=False, azim=AZIM, elev=ELEV) myplot.plot_action_value_function(Q1, title`

### 5.6 Run 2

Our final run uses 500,000 episodes and the accuracy of the action-value function is higher.

```
= mc_estimation(
Q2,monitored_state_action_values2
env, =500_000,
n_episodes=monitored_state_actions,
monitored_state_actions=False) diag
```

`Episode 500000/500000`

```
# last value in monitored_state_actions should be value in Q
= monitored_state_actions[0]; print('msa:', msa)
msa = msa[0]; print('s:', s)
s = msa[1]; print('a:', a)
a -1], Q2[s][a] #monitored_stuff[msa] BUT Q[s][a] monitored_state_action_values2[msa][
```

```
msa: ((21, 7, False), 0)
s: (21, 7, False)
a: 0
```

`(0.9148, 0.9148)`

```
"figure.figsize"] = (18,12)
plt.rcParams[for msa in monitored_state_actions:
plt.plot(monitored_state_action_values2[msa])'Estimated $q_\pi(s,a)$ for some state-actions', fontsize=18)
plt.title('Episodes', fontsize=16)
plt.xlabel('Estimated $q_\pi(s,a)$', fontsize=16)
plt.ylabel(=16)
plt.legend(monitored_state_actions, fontsize plt.show()
```

`="500,000 Steps", wireframe=True, azim=AZIM, elev=ELEV) myplot.plot_action_value_function(Q2, title`

`="500,000 Steps", wireframe=False, azim=AZIM, elev=ELEV) myplot.plot_action_value_function(Q2, title`