# INITIALIZE THE MAIN AGENT CLASS
ai = MERL(args)
print('Running ', args.config.env_choice, 'with config ',
args.config.config, ' State_dim:', args.state_dim, 'Action_dim',
args.action_dim)
time_start = time.time()
###### TRAINING LOOP ########
for gen in range(1, 10000000000): # RUN VIRTUALLY FOREVER
# ONE EPOCH OF TRAINING
popn_fits, pg_fits, test_fits = ai.train(gen, test_tracker)
# PRINT PROGRESS
print('Ep:/Frames', gen, '/', ai.total_frames, 'Popn stat:',
mod.list_stat(popn_fits), 'PG_stat:', mod.list_stat(pg_fits),
'Test_trace:', [pprint(i) for i in ai.test_trace[-5:]], 'FPS:',
pprint(ai.total_frames / (time.time() - time_start)), 'Evo',
args.scheme, 'PS:', args.ps)
if gen % 5 == 0:
print()
print('Test_stat:', mod.list_stat(test_fits), 'SAVETAG: ',
args.savetag)
print('Weight Stats: min/max/average',
pprint(ai.test_bucket[0].get_norm_stats()))
print('Buffer Lens:',
[ag.buffer[0].__len__() for ag in ai.agents] if args.ps
== 'trunk' else [ag.buffer.__len__() for ag in ai.agents])
print()
if args.config.env_choice == 'hyper': from envs.hyper.PowerPlant_env import Fast_Simulator # Main Module needs access to this class for some reason
# INITIALIZE THE MAIN AGENT CLASS
ai = MERL(args)
print('Running ', args.config.env_choice, 'with config ', args.config.config, ' State_dim:', args.state_dim,
'Action_dim', args.action_dim)
time_start = time.time()
###### TRAINING LOOP ########
for gen in range(1, 10000000000): # RUN VIRTUALLY FOREVER
# ONE EPOCH OF TRAINING
popn_fits, pg_fits, test_fits = ai.train(gen, test_tracker)
# PRINT PROGRESS
print('Ep:/Frames', gen, '/', ai.total_frames, 'Popn stat:', mod.list_stat(popn_fits), 'PG_stat:',
mod.list_stat(pg_fits),
'Test_trace:', [pprint(i) for i in ai.test_trace[-5:]], 'FPS:',
pprint(ai.total_frames / (time.time() - time_start)), 'Evo', args.scheme, 'PS:', args.ps
)
if gen % 5 == 0:
print()
print('Test_stat:', mod.list_stat(test_fits), 'SAVETAG: ', args.savetag)
print('Weight Stats: min/max/average', pprint(ai.test_bucket[0].get_norm_stats()))
print('Buffer Lens:', [ag.buffer[0].__len__() for ag in ai.agents] if args.ps == 'trunk' else [ag.buffer.__len__() for ag in ai.agents])
print()
if gen % 10 == 0 and args.rollout_size > 0:
print()
print('Q', pprint(ai.agents[0].algo.q))
if args.config.env_choice == 'hyper': from envs.hyper.PowerPlant_env import Fast_Simulator # Main Module needs access to this class for some reason
# INITIALIZE THE MAIN AGENT CLASS
ai = MERL(args)
print('Running ', args.config.env_choice, 'with config ', args.config.config, ' Predator State_dim:', args.pred_state_dim, 'Prey_state_dim', args.prey_state_dim,
'Action_dim', args.action_dim)
time_start = time.time()
###### TRAINING LOOP ########
for gen in range(1, 10000000000): # RUN VIRTUALLY FOREVER
# ONE EPOCH OF TRAINING
popn_fits, pg_fits, test_fits, prey_score = ai.train(gen, test_tracker, prey_tracker)
# PRINT PROGRESS
print('Ep:/Frames', gen, '/', ai.total_frames, 'Popn stat:', mod.list_stat(popn_fits), 'PG_stat:',
mod.list_stat(pg_fits),
'Test_trace:', [pprint(i) for i in ai.test_trace[-5:]], 'FPS:',
pprint(ai.total_frames / (time.time() - time_start)), 'Evo', args.scheme, 'Prey Score:', prey_score)
#Update elites tracker
if gen >2 and args.popn_size > 0:
#elites_tracker.update([ai.agents[0].evolver.rl_res['elites']], gen)
selects_tracker.update([ai.agents.evolver.rl_res['selects']], gen)
if ai.total_frames > args.frames_bound:
break
###Kill all processes
try: ai.pg_task_pipes[0].send('TERMINATE')
except: None