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()
pass parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir)) sys.path.insert(0, parent_dir) from simple_rl.agents import QLearningAgent, RandomAgent, RMaxAgent from simple_rl.planning import ValueIteration from simple_rl.tasks import GridWorldMDP from simple_rl.run_experiments import run_agents_on_mdp # # Setup MDP. # mdp = GridWorldMDP(width=6, height=6, init_loc=(1, 1), goal_locs=[(6, 6)]) # # # Setup Agents. # ql_agent = QLearningAgent(actions=mdp.get_actions()) # rand_agent = RandomAgent(actions=mdp.get_actions()) # rmax_agent = RMaxAgent(actions=mdp.get_actions(), horizon=3, s_a_threshold=1) # # # Run experiment and make plot. # run_agents_on_mdp([ql_agent, rand_agent, rmax_agent], mdp, instances=5, episodes=100, steps=40, reset_at_terminal=True, # verbose=False) from simple_rl.tasks import FourRoomMDP from simple_rl.tasks.grid_world import grid_visualizer four_room_mdp = FourRoomMDP(9, 9, goal_locs=[(9, 9), (5, 4)], gamma=0.95) # Run experiment and make plot. # four_room_mdp.visualize_value() four_room_mdp.visualize_interaction() # four_room_mdp.visualize_policy()
