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75f28fa Aug 24, 2018
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@quanvuong @joschu @cjm715
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import numpy as np
import sys
from gym.envs.toy_text import discrete
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3
class CliffWalkingEnv(discrete.DiscreteEnv):
"""
This is a simple implementation of the Gridworld Cliff
reinforcement learning task.
Adapted from Example 6.6 (page 132) from Reinforcement Learning: An Introduction
by Sutton and Barto:
http://incompleteideas.net/book/the-book-2nd.html
With inspiration from:
https://github.com/dennybritz/reinforcement-learning/blob/master/lib/envs/cliff_walking.py
The board is a 4x12 matrix, with (using Numpy matrix indexing):
[3, 0] as the start at bottom-left
[3, 11] as the goal at bottom-right
[3, 1..10] as the cliff at bottom-center
Each time step incurs -1 reward, and stepping into the cliff incurs -100 reward
and a reset to the start. An episode terminates when the agent reaches the goal.
"""
metadata = {'render.modes': ['human', 'ansi']}
def __init__(self):
self.shape = (4, 12)
self.start_state_index = np.ravel_multi_index((3, 0), self.shape)
nS = np.prod(self.shape)
nA = 4
# Cliff Location
self._cliff = np.zeros(self.shape, dtype=np.bool)
self._cliff[3, 1:-1] = True
# Calculate transition probabilities and rewards
P = {}
for s in range(nS):
position = np.unravel_index(s, self.shape)
P[s] = {a: [] for a in range(nA)}
P[s][UP] = self._calculate_transition_prob(position, [-1, 0])
P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1])
P[s][DOWN] = self._calculate_transition_prob(position, [1, 0])
P[s][LEFT] = self._calculate_transition_prob(position, [0, -1])
# Calculate initial state distribution
# We always start in state (3, 0)
isd = np.zeros(nS)
isd[self.start_state_index] = 1.0
super(CliffWalkingEnv, self).__init__(nS, nA, P, isd)
def _limit_coordinates(self, coord):
"""
Prevent the agent from falling out of the grid world
:param coord:
:return:
"""
coord[0] = min(coord[0], self.shape[0] - 1)
coord[0] = max(coord[0], 0)
coord[1] = min(coord[1], self.shape[1] - 1)
coord[1] = max(coord[1], 0)
return coord
def _calculate_transition_prob(self, current, delta):
"""
Determine the outcome for an action. Transition Prob is always 1.0.
:param current: Current position on the grid as (row, col)
:param delta: Change in position for transition
:return: (1.0, new_state, reward, done)
"""
new_position = np.array(current) + np.array(delta)
new_position = self._limit_coordinates(new_position).astype(int)
new_state = np.ravel_multi_index(tuple(new_position), self.shape)
if self._cliff[tuple(new_position)]:
return [(1.0, self.start_state_index, -100, False)]
terminal_state = (self.shape[0] - 1, self.shape[1] - 1)
is_done = tuple(new_position) == terminal_state
return [(1.0, new_state, -1, is_done)]
def render(self, mode='human'):
outfile = sys.stdout
for s in range(self.nS):
position = np.unravel_index(s, self.shape)
if self.s == s:
output = " x "
# Print terminal state
elif position == (3, 11):
output = " T "
elif self._cliff[position]:
output = " C "
else:
output = " o "
if position[1] == 0:
output = output.lstrip()
if position[1] == self.shape[1] - 1:
output = output.rstrip()
output += '\n'
outfile.write(output)
outfile.write('\n')