def main():
# Setup MDP, Agents.
mdp = FourRoomMDP(11, 11, goal_locs=[(11, 11)], gamma=0.9, step_cost=0.0)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.4)
viz = parse_args()
# Choose viz type.
viz = "learning"
if viz == "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":
# Solve problem and show agent interaction.
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":
# Run experiment and make plot.
mdp.visualize_learning(ql_agent)
elif viz == "interactive":
mdp.visualize_interaction()
def main():
# Setup MDP, Agents.
mdp = FourRoomMDP(5, 5, goal_locs=[(5, 5)], gamma=0.99, step_cost=0.01)
# mdp = make_grid_world_from_file("octogrid.txt", num_goals=12, randomize=False)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.5)
rm_agent = RMaxAgent(mdp.get_actions())
viz = parse_args()
viz = "learning"
if viz == "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":
# Solve problem and show agent interaction.
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":
# Run experiment and make plot.
mdp.visualize_learning(ql_agent)