state_values = {state: 0
for state in mdp.get_all_states()
} # Initialize state_values
# Run value iteration algo!
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference,
state_values)
# See how our agent performs - e.g. render what is going on when agent choose `optimal` value
s = mdp.reset()
mdp.render()
rewards = [] # Save all rewards to see mean reward.
for _ in range(num_iter):
action = get_optimal_action(mdp, state_values, s, gamma)
new_state, reward, done, _ = mdp.step(action)
rewards += [reward]
s = new_state
mdp.render()
if done:
break
print('Done!')
print(reward)
print('Average reward: ', np.mean(rewards))
# if visualize:
# draw_policy(mdp, state_values)
# Play in Frozen Lake Env
state_values = mdp.get_all_states() # Initialize state_values
# Run value iteration algo!
init_values = {s: 0 for s in state_values}
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference,
init_values)
# See how our agent performs - e.g. render what is going on when agent choose `optimal` value
s = mdp.reset()
mdp.render()
rewards = [] # Save all rewards to see mean reward.
# Your code here!
for t in range(num_iter):
s, r, done, _ = mdp.step(
get_optimal_action(mdp, state_values, s, gamma))
rewards.append(r)
if done:
break
rewards.append(np.sum(rewards))
print('Average reward: ', np.mean(rewards))
if visualize:
draw_policy(mdp, state_values)
# Let's see how it is improving in time.
visualize_step_by_step(mdp, gamma, num_iter, min_difference)
# Express test!
mass_gaming(mdp, gamma, num_iter, 1000, 100)
state_values = {s: 0
for s in mdp.get_all_states()} # Initialize state_values
# Run value iteration algo!
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference,
state_values) # try
# See how our agent performs - e.g. render what is going on when agent choose `optimal` value
s = mdp.reset()
mdp.render()
rewards = [] # Save all rewards to see mean reward.
# Your code here!
max_steps = 10000
for _ in range(max_steps):
act = get_optimal_action(mdp, state_values, s, gamma)
if act is not None:
s, r, done, _ = mdp.step(act)
rewards.append(r)
print('Average reward: ', np.mean(rewards))
if visualize:
draw_policy(mdp, state_values)
# Let's see how it is improving in time.
visualize_step_by_step(mdp, gamma, num_iter, min_difference)
# Express test!
mass_gaming(mdp, gamma, num_iter, 1000, 100)
# Play in Frozen Lake Env
state_values = {s: 0
for s in mdp.get_all_states()} # Initialize state_values
# Run value iteration algo!
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference,
state_values)
# See how our agent performs - e.g. render what is going on when agent choose `optimal` value
s = mdp.reset()
mdp.render()
rewards = [] # Save all rewards to see mean reward.
for step in range(1000):
s, current_reward, is_done, _ = mdp.step(
get_optimal_action(mdp, state_values, s, gamma))
rewards.append(current_reward)
if is_done:
break
print('Average reward: ', np.mean(rewards))
if visualize:
draw_policy(mdp, state_values)
# Let's see how it is improving in time.
#visualize_step_by_step(mdp, gamma, num_iter, min_difference)
# Express test!
mass_gaming(mdp, gamma, num_iter, 1000, 100)
