Artificial Intelligence

""" University of Glasgow Artificial Intelligence 2018-2019 Assessed Exercise Basic demo code for the CUSTOM Open AI Gym problem used in AI (H) '18-'19 """ import gym import numpy as np import time from uofgsocsai import LochLomondEnv # load the class defining the custom Open AI Gym problem import os, sys from helpers import * print("Working dir:"+os.getcwd()) print("Python version:"+sys.version) # Setup the parameters for the specific problem (you can change all of these if you want to) problem_id = 0 # problem_id \in [0:7] generates 8 diffrent problems on which you can train/fine-tune your agent reward_hole = 0.0 # should be less than or equal to 0.0 (you can fine tune this depending on you RL agent choice) is_stochastic = True # should be False for A-star (deterministic search) and True for the RL agent max_episodes = 2000 # you can decide you rerun the problem many times thus generating many episodes... you can learn from them all! max_iter_per_episode = 500 # you decide how many iterations/actions can be executed per episode # Generate the specific problem env = LochLomondEnv(problem_id=problem_id, is_stochastic=False, reward_hole=reward_hole) # Let's visualize the problem/env print(env.desc) # Create a representation of the state space for use with AIMA A-star state_space_locations, state_space_actions, state_initial_id, state_goal_id = env2statespace(env) # Reset the random generator to a known state (for reproducability) np.random.seed(12) #### for e in range(max_episodes): # iterate over episodes observation = env.reset() # reset the state of the env to the starting state for iter in range(max_iter_per_episode): #env.render() # for debugging/develeopment you may want to visualize the individual steps by uncommenting this line action = env.action_space.sample() # your agent goes here (the current agent takes random actions) observation, reward, done, info = env.step(action) # observe what happends when you take the action # TODO: You'll need to add code here to collect the rewards for plotting/reporting in a suitable manner print("e,iter,reward,done =" + str(e) + " " + str(iter)+ " " + str(reward)+ " " + str(done)) # Check if we are done and monitor rewards etc... if(done and reward==reward_hole): env.render() print("We have reached a hole :-( [we can't move so stop trying; just give up]") break if (done and reward == +1.0): env.render() print("We have reached the goal :-) [stop trying to move; we can't]. That's ok we have achived the goal]") break