action_to_take = policy[
grid.pos] if grid.pos in policy else np.random.choice(
grid.possible_actions())
reward = grid.player_take_action(action_to_take)
state_and_rewards.append((grid.pos, reward))
G = 0
states_and_returns = []
for (state, reward) in reversed(state_and_rewards):
G = reward + alpha * G
states_and_returns.append((state, G))
return list(reversed(states_and_returns))[1:]
if __name__ == "__main__":
grid = standard_grid(success_prob=0.8)
states = grid.all_states()
possible_states = [s for s in states if not grid.is_terminal_state(s)]
# Initialize V
print("Initialize steps")
alpha = 0.9
epsilon = 10e-4
iterations = 0
V = {state: 0 for state in states}
counts = {state: 1 for state in states}
policy = {
(2, 0): Action("U", -1, 0),
(1, 0): Action("U", -1, 0),
(0, 0): Action("R", 0, 1),
(0, 1): Action("R", 0, 1),
import numpy as np
import math
from rl.gridworld.utils import standard_grid
from rl.gridworld.Action import Action
from collections import defaultdict
from rl.gridworld.utils import *
if __name__ == "__main__":
grid = standard_grid(0.5, -0.1)
states = grid.all_states()
possible_states = [s for s in states if not grid.is_terminal_state(s)]
# Initialize V, policy
print("Initialize steps")
alpha = 0.9
epsilon = 10e-4
V = {state: 0 for state in states}
policy = {}
for state in possible_states:
grid.set_player_position(state)
policy[state] = np.random.choice(grid.possible_actions())
print_values(V, grid)
print_policy(policy, grid)
# repeat until policy converged
while True:
# policy evaluation
while True:
import numpy as np
import math
from rl.gridworld.utils import standard_grid
from rl.gridworld.Action import Action
from collections import defaultdict
from rl.gridworld.utils import *
if __name__ == "__main__":
grid = standard_grid()
states = grid.all_states()
possible_states = [s for s in states if not grid.is_terminal_state(s)]
# Initialize V, policy
print("Initialize steps")
alpha = 0.9
epsilon = 10e-4
iterations = 0
V = {state: 0 for state in states}
policy = {}
for state in possible_states:
grid.set_player_position(state)
policy[state] = np.random.choice(grid.possible_actions())
print_values(V, grid)
print_policy(policy, grid)
# repeat until policy converged
while True:
iterations += 1
# policy evaluation
import numpy as np
import math
from rl.gridworld.utils import standard_grid
from rl.gridworld.Action import Action
from collections import defaultdict
from rl.gridworld.utils import *
if __name__ == "__main__":
grid = standard_grid(normal_reward=-0.1)
states = grid.all_states()
possible_states = [s for s in states if not grid.is_terminal_state(s)]
# Initialize V
print("Initialize steps")
alpha = 0.9
epsilon = 10e-4
iterations = 0
V = {state: 0 for state in states}
print_values(V, grid)
# repeat until policy converged
# value iteration
while True:
iterations += 1
biggest_change = -math.inf
for state in possible_states:
old_v = V[state]
grid.set_player_position(state)
new_v = -math.inf
for action in grid.possible_actions():
grid.set_player_position(state)