Can you give me an example Q learning pseudo code for a robotic arm application
user
certainly, here is a possible example of a q-learning pseudo-code for a robotic arm application:
```java
state:  axes angles of arm (x,y,z)
action:  joint torques (shoulder, elbow, wrist)
reward:  distance between end effector and target (in meters)

initialize: q-table, random starting values

episode loop:
  receive starting state (x,y,z) for arm
  t = 0
  for t = 1, maxiterations do
    choose action a according to current q-value (e.g., epsilon-greedy or boltzmann distribution)
    apply action a to robot arm (e.g., pd control or equilibrium point control)
    measure new state (x',y',z') using sensors
    measure reward r = ||(x',y',z') - target||
    t = t+1
    update q-table: q(x,y,z,a) += alpha*(r + max_q - q(x,y,z,a)) where alpha is the learning rate
assistant
Certainly, here is a possible example of a Q-learning pseudo-code for a robotic arm application:
```java
State:  axes angles of arm (x,y,z)
Action:  joint torques (shoulder, elbow, wrist)
Reward:  distance between end effector and target (in meters)

Initialize: Q-table, random starting values

Episode Loop:
  Receive starting state (x,y,z) for arm
  t = 0
  for t = 1, MaxIterations do
    Choose action A according to current Q-value (e.g., epsilon-greedy or Boltzmann distribution)
    Apply action A to robot arm (e.g., PD control or equilibrium point control)
    Measure new state (x',y',z') using sensors
    Measure reward R = ||(x',y',z') - target||
    t = t+1
    Update Q-table: Q(x,y,z,A) += alpha*(R + max_Q - Q(x,y,z,A)) where alpha is the learning rate
assistant