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doc/Programs/Reinforcement/reinforcementExample.py

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+"""
+A simple example for Reinforcement Learning using table lookup Q-learning method.
+An agent "o" is on the left of a 1 dimensional world, the treasure is on the rightmost location.
+Run this program and to see how the agent will improve its strategy of finding the treasure.
+View more on my tutorial page: https://morvanzhou.github.io/tutorials/
+"""
+
+import numpy as np
+import pandas as pd
+import time
+
+np.random.seed(2)  # reproducible
+
+
+N_STATES = 6   # the length of the 1 dimensional world
+ACTIONS = ['left', 'right']     # available actions
+EPSILON = 0.9   # greedy police
+ALPHA = 0.1     # learning rate
+GAMMA = 0.9    # discount factor
+MAX_EPISODES = 13   # maximum episodes
+FRESH_TIME = 0.3    # fresh time for one move
+
+
+def build_q_table(n_states, actions):
+    table = pd.DataFrame(
+        np.zeros((n_states, len(actions))),     # q_table initial values
+        columns=actions,    # actions's name
+    )
+    # print(table)    # show table
+    return table
+
+
+def choose_action(state, q_table):
+    # This is how to choose an action
+    state_actions = q_table.iloc[state, :]
+    if (np.random.uniform() > EPSILON) or ((state_actions == 0).all()):  # act non-greedy or state-action have no value
+        action_name = np.random.choice(ACTIONS)
+    else:   # act greedy
+        action_name = state_actions.idxmax()    # replace argmax to idxmax as argmax means a different function in newer version of pandas
+    return action_name
+
+
+def get_env_feedback(S, A):
+    # This is how agent will interact with the environment
+    if A == 'right':    # move right
+        if S == N_STATES - 2:   # terminate
+            S_ = 'terminal'
+            R = 1
+        else:
+            S_ = S + 1
+            R = 0
+    else:   # move left
+        R = 0
+        if S == 0:
+            S_ = S  # reach the wall
+        else:
+            S_ = S - 1
+    return S_, R
+
+
+def update_env(S, episode, step_counter):
+    # This is how environment be updated
+    env_list = ['-']*(N_STATES-1) + ['T']   # '---------T' our environment
+    if S == 'terminal':
+        interaction = 'Episode %s: total_steps = %s' % (episode+1, step_counter)
+        print('\r{}'.format(interaction), end='')
+        time.sleep(2)
+        print('\r                                ', end='')
+    else:
+        env_list[S] = 'o'
+        interaction = ''.join(env_list)
+        print('\r{}'.format(interaction), end='')
+        time.sleep(FRESH_TIME)
+
+
+def rl():
+    # main part of RL loop
+    q_table = build_q_table(N_STATES, ACTIONS)
+    for episode in range(MAX_EPISODES):
+        step_counter = 0
+        S = 0
+        is_terminated = False
+        update_env(S, episode, step_counter)
+        while not is_terminated:
+
+            A = choose_action(S, q_table)
+            S_, R = get_env_feedback(S, A)  # take action & get next state and reward
+            q_predict = q_table.loc[S, A]
+            if S_ != 'terminal':
+                q_target = R + GAMMA * q_table.iloc[S_, :].max()   # next state is not terminal
+            else:
+                q_target = R     # next state is terminal
+                is_terminated = True    # terminate this episode
+
+            q_table.loc[S, A] += ALPHA * (q_target - q_predict)  # update
+            S = S_  # move to next state
+
+            update_env(S, episode, step_counter+1)
+            step_counter += 1
+    return q_table
+
+
+if __name__ == "__main__":
+    q_table = rl()
+    print('\r\nQ-table:\n')
+print(q_table)