# Q-Learning vs. SARSA

Two fundamental RL algorithms, both remarkably useful, even today. One of the primary reasons for their popularity is that they are simple, because by default they only work with discrete state and action spaces. Of course it is possible to improve them to work with continuous state/action spaces, but consider discretizing to keep things rediculously simple.

In this workshop I’m going to reproduce the cliffworld example in the book. In the future I will extend and expand on this so you can develop your own algorithms and environments.

## A note on usage

Note that this notebook might not work on your machine because simple_rl forces TkAgg on some machines. See https://github.com/david-abel/simple_rl/issues/40

Also, Pygame is notoriously picky and expects loads of compiler/system related libraries.

I managed to get this working on the following notebook:

```
docker run -it -p 8888:8888 jupyter/scipy-notebook:54462805efcb
```

This code is untested on any other notebook.

TODO: migrate away from simple rl and pygame. TODO: Create dedicated q-learning and sarsa notebooks.

```
!pip install pygame==1.9.6 pandas==1.0.5 matplotlib==3.2.1 > /dev/null
!pip install --upgrade git+git://github.com/david-abel/simple_rl.git@77c0d6b910efbe8bdd5f4f87337c5bc4aed0d79c > /dev/null
import matplotlib
matplotlib.use("agg", force=True)
```

```
Running command git clone -q git://github.com/david-abel/simple_rl.git /tmp/pip-req-build-zgmzgexc
```

## SARSA Agent

Now that lot is sorted, the next issue is that simple_rl doesn’t have a SARSA agent. So I had to implement one. This is complicated by the abstractions enforced by `simple_rl`

, but the key section is in the `update`

function. This is choosing and updating an action at the same time. This is different to Q-learning, in that the action chosen is independent of the action that is updated.

I’ll make this clearer in future updates to these notebooks.

```
'''
SARSAAgent.py
Implementation of a SARSA agent for simple-rl
'''
# Python imports.
import numpy as np
import math
from collections import defaultdict
# Other imports.
from simple_rl.agents import Agent, QLearningAgent
from simple_rl.tasks import GridWorldMDP
class SARSAAgent(QLearningAgent):
def __init__(self, actions, goal_reward, name="SARSA",
alpha=0.1, gamma=0.99, epsilon=0.1, explore="uniform", anneal=False):
self.goal_reward = goal_reward
QLearningAgent.__init__(
self,
actions=list(actions),
name=name,
alpha=alpha,
gamma=gamma,
epsilon=epsilon,
explore=explore,
anneal=anneal)
def policy(self, state):
return self.get_max_q_action(state)
def act(self, state, reward, learning=True):
'''
This is mostly the same as the base QLearningAgent class. Except that
the update procedure now generates the action.
'''
if learning:
action = self.update(
self.prev_state, self.prev_action, reward, state)
else:
if self.explore == "softmax":
# Softmax exploration
action = self.soft_max_policy(state)
else:
# Uniform exploration
action = self.epsilon_greedy_q_policy(state)
self.prev_state = state
self.prev_action = action
self.step_number += 1
# Anneal params.
if learning and self.anneal:
self._anneal()
return action
def update(self, state, action, reward, next_state):
'''
Args:
state (State)
action (str)
reward (float)
next_state (State)
Summary:
Updates the internal Q Function according to the Bellman Equation
using a SARSA update
'''
if self.explore == "softmax":
# Softmax exploration
next_action = self.soft_max_policy(next_state)
else:
# Uniform exploration
next_action = self.epsilon_greedy_q_policy(next_state)
# Update the Q Function.
prev_q_val = self.get_q_value(state, action)
next_q_val = self.get_q_value(next_state, next_action)
self.q_func[state][action] = prev_q_val + self.alpha * \
(reward + self.gamma * next_q_val - prev_q_val)
return next_action
```

```
Warning: Tensorflow not installed.
Warning: OpenAI gym not installed.
```

## Experiment

Now I’m ready to run the experiment with the helpers from `simple_rl`

. Basically I’m training an agent for a maximum of 100 steps, for 500 episodes, averaging over 100 repeats.

Feel free to tinker with the settimgs.

```
import pandas as pd
import numpy as np
from simple_rl.agents import QLearningAgent, RandomAgent
from simple_rl.tasks import GridWorldMDP
from simple_rl.run_experiments import run_single_agent_on_mdp
np.random.seed(42)
instances = 100
n_episodes = 500
alpha = 0.1
epsilon = 0.1
# Setup MDP, Agents.
mdp = GridWorldMDP(
width=10, height=4, init_loc=(1, 1), goal_locs=[(10, 1)],
lava_locs=[(x, 1) for x in range(2, 10)], is_lava_terminal=True, gamma=1.0, walls=[], slip_prob=0.0, step_cost=1.0, lava_cost=100.0)
print("Q-Learning")
rewards = np.zeros((n_episodes, instances))
for instance in range(instances):
ql_agent = QLearningAgent(
mdp.get_actions(),
epsilon=epsilon,
alpha=alpha)
# mdp.visualize_learning(ql_agent, delay=0.0001)
print(" Instance " + str(instance) + " of " + str(instances) + ".")
terminal, num_steps, reward = run_single_agent_on_mdp(
ql_agent, mdp, episodes=n_episodes, steps=100)
rewards[:, instance] = reward
df = pd.DataFrame(rewards.mean(axis=1))
df.to_json("q_learning_cliff_rewards.json")
print("SARSA")
rewards = np.zeros((n_episodes, instances))
for instance in range(instances):
sarsa_agent = SARSAAgent(
mdp.get_actions(),
goal_reward=0,
epsilon=epsilon,
alpha=alpha)
# mdp.visualize_learning(sarsa_agent, delay=0.0001)
print(" Instance " + str(instance) + " of " + str(instances) + ".")
terminal, num_steps, reward = run_single_agent_on_mdp(
sarsa_agent, mdp, episodes=n_episodes, steps=100)
rewards[:, instance] = reward
df = pd.DataFrame(rewards.mean(axis=1))
df.to_json("sarsa_cliff_rewards.json")
```

```
Q-Learning
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```

## Results

Now you can plot the results for each of the agents.

```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
data_files = [("Q-Learning", "q_learning_cliff_rewards.json"),
("SARSA", "sarsa_cliff_rewards.json")]
fig, ax = plt.subplots()
for j, (name, data_file) in enumerate(data_files):
df = pd.read_json(data_file)
x = range(len(df))
y = df.sort_index().values
ax.plot(x,
y,
linestyle='solid',
label=name)
ax.set_xlabel('Episode')
ax.set_ylabel('Averaged Sum of Rewards')
ax.legend(loc='lower right')
plt.show()
```

## Policy Results

If you’re not in a notebook, then you can use `simple_rl`

to visualize your policy below.

```
# mdp.visualize_agent(sarsa_agent)
```