def popBackUntil(self, pop_back_until):
if DEBUG_MODE and STACK_PRINTING:
dump_incremental(
"StackBEFORE" + str(self.t) + str(self.current_feature),
self.Stack)
SSAimplementor.popBackUntil(self, pop_back_until)
self.update_pointers()
if DEBUG_MODE and STACK_PRINTING:
dump_incremental("Stack" + str(self.t) + str(self.current_feature),
self.Stack)
agent.num_episodes = 0
agent.learner.printDevelopmentAtari(frames=0)
agent.index = 0
starttime = time.time()
#print(agent.learner.__dict__)
for i in range(agent.index,len(envs)):
env=envs[i]
print("starting mountain car environment")
if not interrupted:
agent.taskblock_t=0
agent.learner.new_task([i])
for item in env.__dict__.items():
print(item)
while agent.taskblock_t<FRAMES_PER_TASK:
print("starting new episode at taskblock_t: ", agent.taskblock_t)
consumed_steps=perform_episode(args.VISUAL, env, agent, args.run*100000+agent.num_episodes, agent.total_t)
agent.taskblock_t+=consumed_steps
agent.total_t+=consumed_steps # need to add because primitive data types not passed by reference
agent.num_episodes+=1
agent.learner.printDevelopmentAtari(frames=agent.total_t)
walltime_consumed = time.time() - starttime
if walltime_consumed >= 0.9*walltime:
break
agent.learner.end_task()
agent.learner.save(filename)
dump_incremental(filename + "_agent", agent)
print("stopping at time ", walltime_consumed)
exit(0)
def save_stats(self, filename):
if self.stats is not None:
dump_incremental(filename + '_stats_object', self.stats)
) # network has full action set, but only uses minimal for each task
agent.total_t = 0
agent.num_episodes = 0
agent.learner.printDevelopmentAtari(frames=0)
agent.index = 0
starttime = time.time()
if args.experiment_type == "print_diversity":
data = random_data()
output_div = []
performance_diversities = []
div = agent.learner.get_output_diversity(data, metric_type="totalvar")
print("div = " + str(div))
output_div.append(div)
performance_diversities.append(None)
dump_incremental(filename + "_outputdiversity_totalvar",
(output_div, performance_diversities))
exit(0)
elif args.experiment_type == "randomBaseline":
iterations = 10
randomBaselines = {}
total_episodes = 0
total_t = 0
for i in indices:
terminal_file = [
open("terminal_x_file" + str(i) + ".txt", "w+"),
open("terminal_theta_file" + str(i) + ".txt", "w+")
]
performances = []
j = indices[i]
env = envs[j]
print("task ", j)