env = RewardNegativeDeath(env, death_factor=2)
env = ObservationReshape(env)
# create agent
model = CartpoleNetwork(learning_rate=LEARNING_RATE,
discount_factor=DISCOUNT_FACTOR,
input_shape=(env.observation_space.shape[0], ),
output_shape=env.action_space.n)
agent = DQNAgent(actions=env.action_space.n,
expl_max=EXPLORATION_MAX,
expl_min=EXPLORATION_MIN,
expl_decay=EXPLORATION_DECAY,
model=model,
memory_size=MEMORY_SIZE,
batch_size=BATCH_SIZE)
# get and parse user args
args = Parser.parseargs(defaultTrainIterations=10000,
defaultEvalIterations=10)
if args.load:
agent.load(env, args.loadversion)
if args.train != 0:
#agent.init_fill_memory(env, 50000)
agent.train(env, args.train, train_s=1, save_i=MODEL_SAVE_EVERY)
if args.eval != 0:
print("Evaluation results (higher scores are better):")
agent.evaluate(env, args.eval)
if args.save:
agent.save(env, args.saveversion)
if args.render:
agent.render_episode(env, random_action=args.renderrandom)
# close env
env.close()
