pip install or git clone bettermdptools.
pip3 install bettermdptools
git clone https://github.com/jlm429/bettermdptools
Starter code to get up and running on OpenAI's frozen lake environment. See bettermdptools/examples for more.
import gym
import pygame
from algorithms.rl import RL
from examples.test_env import TestEnv
frozen_lake = gym.make('FrozenLake8x8-v1', render_mode=None)
# Q-learning
Q, V, pi, Q_track, pi_track = RL(frozen_lake.env).q_learning()
test_scores = TestEnv.test_env(env=frozen_lake.env, render=True, user_input=False, pi=pi)
#grid search
epsilon_decay = [.4, .7, .9]
iters = [500, 5000, 50000]
GridSearch.Q_learning_grid_search(frozen_lake.env, epsilon_decay, iters)
#plot state values
frozen_lake = gym.make('FrozenLake8x8-v1', render_mode=None)
V, V_track, pi = Planner(frozen_lake.env.P).value_iteration()
Plots.grid_values_heat_map(V, "State Values")
class bettermdptools.algorithms.planner.Planner(P)
Class that contains functions related to planning algorithms (Value Iteration, Policy Iteration). Planner init expects a reward and transitions matrix P, which is nested dictionary OpenAI Gym style discrete environment where P[state][action] is a list of tuples (probability, next state, reward, terminal).
Frozen Lake VI example:
env = gym.make('FrozenLake8x8-v1')
V, V_track, pi = Planner(env.P).value_iteration()
function bettermdptools.algorithms.planner.Planner.value_iteration(self,
gamma=1.0, n_iters=1000, theta=1e-10) -> V, V_track, pi
PARAMETERS:
gamma {float}: Discount factor
n_iters {int}: Number of iterations
theta {float}: Convergence criterion for value iteration. State values are considered to be converged when the maximum difference between new and previous state values is less than theta. Stops at n_iters or theta convergence - whichever comes first.
RETURNS:
V {numpy array}, shape(possible states): State values array
V_track {numpy array}, shape(n_episodes, nS): Log of V(s) for each iteration
pi {lambda}, input state value, output action value: Policy mapping states to actions.
function bettermdptools.algorithms.planner.Planner.policy_iteration(self,
gamma=1.0, n_iters=1000, theta=1e-10) -> V, V_track, pi
PARAMETERS:
gamma {float}: Discount factor
n_iters {int}: Number of iterations
theta {float}: Convergence criterion for policy evaluation. State values are considered to be converged when the maximum difference between new and previous state values is less than theta.
RETURNS:
V {numpy array}, shape(possible states): State values array
V_track {numpy array}, shape(n_episodes, nS): Log of V(s) for each iteration
pi {lambda}, input state value, output action value: Policy mapping states to actions.
class bettermdptools.algorithms.rl.RL(env)
Class that contains functions related to reinforcement learning algorithms. RL init expects an OpenAI environment (env).
The RL algorithms (Q-learning, SARSA) work out of the box with any OpenAI Gym environment that have single discrete valued state spaces, like frozen lake. A lambda function is required to convert state spaces not in this format. For example, blackjack is "a 3-tuple containing: the player’s current sum, the value of the dealer’s one showing card (1-10 where 1 is ace), and whether the player holds a usable ace (0 or 1)."
Here, blackjack.convert_state_obs changes the 3-tuple into a discrete space with 280 states by concatenating player states 0-27 (hard 4-21 & soft 12-21) with dealer states 0-9 (2-9, ten, ace).
self.convert_state_obs = lambda state, done: ( -1 if done else int(f"{state[0] + 6}{(state[1] - 2) % 10}") if state[2] else int(f"{state[0] - 4}{(state[1] - 2) % 10}"))
Since n_states is modified by the state conversion, this new value is passed in along with n_actions, and convert_state_obs.
# Q-learning
Q, V, pi, Q_track, pi_track = RL(blackjack.env).q_learning(blackjack.n_states, blackjack.n_actions, blackjack.convert_state_obs)
function bettermdptools.algorithms.rl.RL.q_learning(self, nS=None, nA=None,
convert_state_obs=lambda state, done: state,
gamma=.99, init_alpha=0.5, min_alpha=0.01, alpha_decay_ratio=0.5,
init_epsilon=1.0, min_epsilon=0.1, epsilon_decay_ratio=0.9, n_episodes=10000)
-> Q, V, pi, Q_track, pi_track
PARAMETERS:
nS {int}: Number of states
nA {int}: Number of available actions
convert_state_obs {lambda}: The state conversion utilized in BlackJack ToyText problem. Returns three state tuple as one of the 280 converted states.
gamma {float}, default = 0.99: Discount factor
init_alpha {float}, default = 0.5: Learning rate
min_alpha {float}, default = 0.01: Minimum learning rate
alpha_decay_ratio {float}, default = 0.5: Decay schedule of learing rate for future iterations
init_epsilon {float}, default = 0.1: Initial epsilon value for epsilon greedy strategy. Chooses max(Q) over available actions with probability 1-epsilon.
min_epsilon {float}, default = 0.1: Minimum epsilon. Used to balance exploration in later stages.
epsilon_decay_ratio {float}, default = 0.9: Decay schedule of epsilon for future iterations
n_episodes {int}, default = 10000: Number of episodes for the agent
RETURNS:
Q {numpy array}, shape(nS, nA): Final action-value function Q(s,a)
pi {lambda}, input state value, output action value: Policy mapping states to actions.
V {numpy array}, shape(nS): State values array
Q_track {numpy array}, shape(n_episodes, nS, nA): Log of Q(s,a) for each episode
pi_track {list}, len(n_episodes): Log of complete policy for each episode
function bettermdptools.algorithms.rl.RL.sarsa(self, nS=None, nA=None,
convert_state_obs=lambda state, done: state,
gamma=.99, init_alpha=0.5, min_alpha=0.01, alpha_decay_ratio=0.5,
init_epsilon=1.0, min_epsilon=0.1, epsilon_decay_ratio=0.9, n_episodes=10000)
-> Q, V, pi, Q_track, pi_track
PARAMETERS:
nS {int}: Number of states
nA {int}: Number of available actions
convert_state_obs {lambda}: The state conversion utilized in BlackJack ToyText problem. Returns three state tuple as one of the 280 converted states.
gamma {float}, default = 0.99: Discount factor
init_alpha {float}, default = 0.5: Learning rate
min_alpha {float}, default = 0.01: Minimum learning rate
alpha_decay_ratio {float}, default = 0.5: Decay schedule of learing rate for future iterations
init_epsilon {float}, default = 0.1: Initial epsilon value for epsilon greedy strategy. Chooses max(Q) over available actions with probability 1-epsilon.
min_epsilon {float}, default = 0.1: Minimum epsilon. Used to balance exploration in later stages.
epsilon_decay_ratio {float}, default = 0.9: Decay schedule of epsilon for future iterations
n_episodes {int}, default = 10000: Number of episodes for the agent
RETURNS:
Q {numpy array}, shape(nS, nA): Final action-value function Q(s,a)
pi {lambda}, input state value, output action value: Policy mapping states to actions.
V {numpy array}, shape(nS): State values array
Q_track {numpy array}, shape(n_episodes, nS, nA): Log of Q(s,a) for each episode
pi_track {list}, len(n_episodes): Log of complete policy for each episode
Base class.
class bettermdptools.callbacks.Callbacks():
RL algorithms SARSA and Q-learning have callback hooks for episode number, begin, end, and env. step.
class bettermdptools.callbacks.MyCallbacks(Callbacks):
To create a callback, override one of the callback functions in the child class MyCallbacks. Here, on_episode prints the episode number every 1000 episodes.
class MyCallbacks(Callbacks):
def __init__(self):
pass
def on_episode(self, caller, episode):
if episode % 1000 == 0:
print(" episode=", episode)
Or, you can use the add_to decorator and define the override outside of the class definition.
from decorators.decorators import add_to
from callbacks.callbacks import MyCallbacks
@add_to(MyCallbacks)
def on_episode_end(self, caller):
print("toasty!")
function on_episode(self, caller, episode):
PARAMETERS:
caller (RL type): Calling object
episode {int}: Current episode from caller
function on_episode_begin(self, caller):
PARAMETERS:
caller (RL type): Calling object
function on_episode_end(self, caller):
PARAMETERS:
caller (RL type): Calling object
function on_env_step(self, caller):
PARAMETERS:
caller (RL type): Calling object
Pull requests are welcome.
- Fork bettermdptools.
- Create a branch (
git checkout -b branch_name) - Commit changes (
git commit -m "Comments") - Push to branch (
git push origin branch_name) - Open a pull request