To develop a Python program to evaluate the given policy.

The bandit slippery walk problem is a reinforcement learning problem in which an agent must learn to navigate a 7-state environment in order to reach a goal state. The environment is slippery, so the agent has a chance of moving in the opposite direction of the action it takes.

The environment has 7 states:

• Two Terminal States: G: The goal state & H: A hole state.
• Five Transition states / Non-terminal States including S: The starting state.

The agent can take two actions:

• R: Move right.
• L: Move left.

The transition probabilities for each action are as follows:

• 50% chance that the agent moves in the intended direction.
• 33.33% chance that the agent stays in its current state.
• 16.66% chance that the agent moves in the opposite direction.

For example, if the agent is in state S and takes the "R" action, then there is a 50% chance that it will move to state 4, a 33.33% chance that it will stay in state S, and a 16.66% chance that it will move to state 2.

The agent receives a reward of +1 for reaching the goal state (G). The agent receives a reward of 0 for all other states.

def policy_evaluation(pi, P, gamma=1.0, theta=1e-10):
    prev_V = np.zeros(len(P), dtype=np.float64)
# code  to evaluate the given policy
    while True:
      V=np.zeros(len(P),dtype=np.float64)
      for s in range(len(P)):
        for prob, next_state, reward, done in P[s][pi(s)]:
          V[s]+=prob*(reward+gamma+prev_V[next_state]*(not done))
      if np.max(np.abs(prev_V-V))<theta:
        break
      prev_V=V.copy()
      return V

# Code to evaluate the first policy
V1 = policy_evaluation(pi_1, P,gamma=0.99)
print_state_value_function(V1, P, n_cols=7, prec=5)

# Code to evaluate the second policy
V2 = policy_evaluation(pi_2, P)
print_state_value_function(V2, P, n_cols=7, prec=5)

# Comparing the two policies
if(np.sum(V1>=V2)==7):
  print("The first policy is the better policy")
elif(np.sum(V2>=V1)==7):
  print("The second policy is the better policy")
else:
  print("Both policies have their merits.")

Thus, a Python program is developed to evaluate the given policy.