def main(open_plot=True):
# Taxi initial state attributes..
agent = {"x": 1, "y": 1, "has_passenger": 0}
passengers = [{"x": 3, "y": 2, "dest_x": 2, "dest_y": 3, "in_taxi": 0}]
walls = []
mdp = TaxiOOMDP(width=4,
height=4,
agent=agent,
walls=walls,
passengers=passengers)
# Agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
viz = False
if viz:
# Visualize Taxi.
run_single_agent_on_mdp(ql_agent, mdp, episodes=50, steps=1000)
mdp.visualize_agent(ql_agent)
else:
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=10,
episodes=1,
steps=500,
reset_at_terminal=True,
open_plot=open_plot)
def main(open_plot=True):
# Taxi initial state attributes..
agent = {"x":1, "y":1, "has_passenger":0}
passengers = [{"x":3, "y":2, "dest_x":2, "dest_y":3, "in_taxi":0}]
walls = []
mdp = TaxiOOMDP(width=4, height=4, agent=agent, walls=walls, passengers=passengers)
# Agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
viz = False
if viz:
# Visualize Taxi.
run_single_agent_on_mdp(ql_agent, mdp, episodes=50, steps=1000)
mdp.visualize_agent(ql_agent)
else:
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent], mdp, instances=10, episodes=1, steps=500, reset_at_terminal=True, open_plot=open_plot)
import srl_example_setup
from simple_rl.agents import QLearnerAgent, RandomAgent
from simple_rl.tasks import TaxiOOMDP, BlockDudeOOMDP
from simple_rl.run_experiments import run_agents_on_mdp, run_single_agent_on_mdp
# Taxi initial state attributes..
agent = {"x": 1, "y": 1, "has_passenger": 0}
passengers = [{"x": 3, "y": 2, "dest_x": 2, "dest_y": 3, "in_taxi": 0}]
walls = []
mdp = TaxiOOMDP(width=4,
height=4,
agent=agent,
walls=walls,
passengers=passengers)
ql_agent = QLearnerAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
viz = False
if viz:
# Visualize Taxi.
run_single_agent_on_mdp(ql_agent, mdp, episodes=50, steps=1000)
mdp.visualize_agent(ql_agent)
else:
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=10,
episodes=100,
steps=150,
reset_at_terminal=True)