def main(open_plot=True):
# Setup MDP.
mdp = GridWorldMDP(width=8,
height=3,
init_loc=(1, 1),
goal_locs=[(8, 3)],
lava_locs=[(4, 2)],
gamma=0.95,
walls=[(2, 2)],
slip_prob=0.05)
# Make agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=20,
episodes=300,
steps=20,
open_plot=open_plot,
track_success=True,
success_reward=1)
def main(open_plot=True):
# Setup MDP.
mdp = GridWorldMDP(width=4,
height=3,
init_loc=(1, 1),
goal_locs=[(4, 3)],
lava_locs=[(4, 2)],
gamma=0.95,
walls=[(2, 2)],
slip_prob=0.05)
# Make agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
tabular_agent = CherryQAgent(mdp,
model=lambda *x: ActionValueFunction(*x, init=1.0),
name='Tabular',
lr=0.7)
linear_agent = CherryQAgent(mdp,
model=lambda *x: nn.Linear(*x),
name='Linear',
lr=0.1)
mlp_agent = CherryQAgent(mdp,
model=lambda *x: MLP(*x),
name='MLP',
lr=0.07)
# Run experiment and make plot.
agents = [rand_agent, ql_agent, tabular_agent, linear_agent, mlp_agent]
run_agents_on_mdp(agents,
mdp,
instances=10,
episodes=50,
steps=50,
open_plot=open_plot)
def main(open_plot=True):
# Setup MDP, Agents.
mdp = GridWorldMDP(width=10, height=10, init_loc=(1, 1), goal_locs=[(10, 10)])
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
abstr_identity_agent = AbstractionWrapper(QLearningAgent, agent_params={"epsilon":0.9, "actions":mdp.get_actions()})
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent, abstr_identity_agent], mdp, instances=5, episodes=100, steps=150, open_plot=open_plot)
def main(open_plot=True):
# Setup MDP, Agents.
mdp = GridWorldMDP(width=10, height=10, init_loc=(1, 1), goal_locs=[(10, 10)])
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
abstr_identity_agent = AbstractionWrapper(QLearningAgent, agent_params={"epsilon":0.9}, actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent, abstr_identity_agent], mdp, instances=5, episodes=100, steps=150, open_plot=open_plot)
def main():
# Setup MDP.
actual_args = {
"width":
10,
"height":
10,
"init_loc": (1, 1),
"goal_locs": [(10, 10)],
"lava_locs": [(1, 10), (3, 10), (5, 10), (7, 10), (9, 10)],
"gamma":
0.9,
"walls": [
(2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9),
(4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9),
(6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9),
(8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9)
],
"slip_prob":
0.01,
"lava_cost":
1.0,
"step_cost":
0.1
}
mdp = GridWorldMDP(**actual_args)
# Initialize the custom Q function for a q-learning agent. This should be equivalent to potential shaping.
# This should cause the Q agent to learn more quickly.
custom_q = defaultdict(lambda: defaultdict(lambda: 0))
custom_q[GridWorldState(5, 1)]['right'] = 1.0
custom_q[GridWorldState(2, 1)]['right'] = 1.0
# Make a normal q-learning agent and another initialized with the custom_q above.
# Finally, make a random agent to compare against.
ql_agent = QLearningAgent(actions=mdp.get_actions(),
epsilon=0.2,
alpha=0.4)
ql_agent_pot = QLearningAgent(actions=mdp.get_actions(),
epsilon=0.2,
alpha=0.4,
custom_q_init=custom_q,
name="PotQ")
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, ql_agent_pot, rand_agent],
mdp,
instances=2,
episodes=60,
steps=200,
open_plot=True,
verbose=True)
def main(open_plot=True):
# Setup MDP.
mdp = GridWorldMDP(width=4, height=3, init_loc=(1, 1), goal_locs=[(4, 3)], lava_locs=[(4, 2)], gamma=0.95, walls=[(2, 2)], slip_prob=0.05)
# Make agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent], mdp, instances=10, episodes=50, steps=10, open_plot=open_plot)
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=4, height=3, init_loc=(1, 1), goal_locs=[(4, 3)], lava_locs=[(4, 2)], gamma=0.95, walls=[(2, 2)], slip_prob=0.1)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.2)
viz = parse_args()
# Choose viz type.
viz = "value"
if viz == "value":
# --> Color corresponds to higher value.
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
value_iter = ValueIteration(mdp)
value_iter.run_vi()
policy = value_iter.policy
mdp.visualize_policy(policy)
elif viz == "agent":
# --> Press <spacebar> to advance the agent.
# First let the agent solve the problem and then visualize the agent's resulting policy.
print("\n", str(ql_agent), "interacting with", str(mdp))
run_single_agent_on_mdp(ql_agent, mdp, episodes=500, steps=200)
mdp.visualize_agent(ql_agent)
elif viz == "learning":
# --> Press <r> to reset.
# Show agent's interaction with the environment.
mdp.visualize_learning(ql_agent, delay=0.005, num_ep=500, num_steps=200)
elif viz == "interactive":
# Press <1>, <2>, <3>, and so on to execute action 1, action 2, etc.
mdp.visualize_interaction()
def main(open_plot=True):
# Setup MDP, Agents.
mdp = GridWorldMDP(width=10,
height=10,
init_loc=(1, 1),
goal_locs=[(10, 10)],
gamma=0.95)
dq_agent = DoubleQAgent(actions=mdp.get_actions())
ql_agent = QLearnerAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([dq_agent, ql_agent, rand_agent],
mdp,
instances=50,
episodes=150,
steps=100,
open_plot=open_plot)
def main(open_plot=True):
# Setup MDP, Agents.
mdp = GridWorldMDP(width=4,
height=3,
init_loc=(1, 1),
goal_locs=[(4, 3)],
gamma=0.95,
walls=[(2, 2)])
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=10,
episodes=1,
steps=20,
open_plot=open_plot)
def main(open_plot=True):
# Setup MDP.
mdp = GridWorldMDP(width=4,
height=3,
init_loc=(1, 1),
goal_locs=[(4, 3)],
lava_locs=[(4, 2)],
gamma=0.95,
walls=[(2, 2)])
# Make agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=5,
episodes=50,
steps=25,
open_plot=open_plot,
track_disc_reward=False)
def main(open_plot=True):
# Setup MDP.
mdp = GridWorldMDP(width=4,
height=3,
init_loc=(1, 1),
goal_locs=[(4, 3)],
lava_locs=[(4, 2)],
gamma=0.95,
walls=[(2, 2)])
# Make agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=5,
episodes=50,
steps=25,
open_plot=open_plot)
# Reproduce the experiment.
reproduce_from_exp_file(exp_name=str(mdp), open_plot=open_plot)
from simple_rl.agents import QLearningAgent, RandomAgent, RMaxAgent
from simple_rl.tasks import GridWorldMDP
from simple_rl.run_experiments import run_agents_on_mdp
# Setup MDP.
mdp = GridWorldMDP(width=4,
height=3,
init_loc=(1, 1),
goal_locs=[(4, 3)],
lava_locs=[(4, 2)],
gamma=0.95,
walls=[(2, 2)],
slip_prob=0.05)
# Setup Agents.
ql_agent = QLearningAgent(actions=mdp.get_actions())
rmax_agent = RMaxAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rmax_agent, rand_agent],
mdp,
instances=5,
episodes=50,
steps=10)
#!/usr/bin/env python
# Other imports.
import srl_example_setup
from simple_rl.agents import QLearnerAgent, RandomAgent, RMaxAgent
from simple_rl.tasks import GridWorldMDP
from simple_rl.run_experiments import run_agents_on_mdp
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, init_loc=(1, 1), goal_locs=[(6, 6)])
rmax_agent = RMaxAgent(actions=mdp.get_actions())
ql_agent = QLearnerAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
run_agents_on_mdp([ql_agent, rand_agent],
mdp,
instances=10,
episodes=30,
steps=50,
reset_at_terminal=True)
slip_prob=0.4,
step_cost=args.step_cost,
rand_init=True)
mdps.append(mdp)
mdp_list = MDPList(mdps)
thres_sm = args.thres_sm
thres_lg = args.thres_lg
ql_agent = QLearningAgent(actions=mdp_list.get_actions(),
gamma=mdp_list.get_gamma())
rmax_agent = TabularRMaxAgent(states=mdp.states,
state_map=mdp.state_map,
actions=mdp.get_actions(),
s_a_threshold=thres_lg,
greedy=args.greedy,
gamma=mdp_list.get_gamma())
multi_agent = MultiTaskRMaxAgent(states=mdp.states,
state_map=mdp.state_map,
actions=mdp.get_actions(),
thres_sm=thres_sm,
thres_lg=thres_lg,
t1=args.t1 + 1,
model_gap=args.model_gap,
greedy=args.greedy,
xi=args.xi,
gamma=mdp_list.get_gamma())
pattern_agent = PatternLearningAgent(states=mdp.states,