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Tiny fix for other envs

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This commit is contained in:
Dominik Moritz Roth 2022-11-07 13:23:55 +01:00
parent 302dbf6dde
commit 4ec5c65cf2
2 changed files with 35 additions and 33 deletions
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2
metastable_baselines/ppo/policies.py
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@ -136,6 +136,8 @@ class ActorCriticPolicy(BasePolicy):
self.normalize_images = normalize_images self.normalize_images = normalize_images
self.log_std_init = log_std_init self.log_std_init = log_std_init
# Keyword arguments for gSDE distribution # Keyword arguments for gSDE distribution
if dist_kwargs == None:
dist_kwargs = {}
if use_sde: if use_sde:
add_dist_kwargs = { add_dist_kwargs = {
'use_sde': True, 'use_sde': True,

66
test.py
View File

@ -24,49 +24,49 @@ def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=1_000_000, showRes=Tru
env = gym.make(env_name) env = gym.make(env_name)
use_sde = True use_sde = True
# th.autograd.set_detect_anomaly(True) # th.autograd.set_detect_anomaly(True)
sac = SAC( #sac = SAC(
MlpPolicySAC, # MlpPolicySAC,
env, # env,
# KLProjectionLayer(trust_region_coeff=0.01), # KLProjectionLayer(trust_region_coeff=0.01),
#projection=WassersteinProjectionLayer(trust_region_coeff=0.01), #projection=WassersteinProjectionLayer(trust_region_coeff=0.01),
policy_kwargs={'dist_kwargs': {'neural_strength': Strength.NONE, 'cov_strength': Strength.DIAG, 'parameterization_type': # policy_kwargs={'dist_kwargs': {'neural_strength': Strength.NONE, 'cov_strength': Strength.DIAG, 'parameterization_type':
ParametrizationType.NONE, 'enforce_positive_type': EnforcePositiveType.ABS, 'prob_squashing_type': ProbSquashingType.NONE}}, # ParametrizationType.NONE, 'enforce_positive_type': EnforcePositiveType.ABS, 'prob_squashing_type': ProbSquashingType.NONE}},
verbose=0, # verbose=0,
tensorboard_log=root_path+"/logs_tb/" + # tensorboard_log=root_path+"/logs_tb/" +
env_name+"/sac"+(['', '_sde'][use_sde])+"/", # env_name+"/sac"+(['', '_sde'][use_sde])+"/",
learning_rate=3e-4, # 3e-4, # learning_rate=3e-4, # 3e-4,
gamma=0.99, # gamma=0.99,
#gae_lambda=0.95, #gae_lambda=0.95,
#normalize_advantage=True, #normalize_advantage=True,
#ent_coef=0.1, # 0.1 #ent_coef=0.1, # 0.1
#vf_coef=0.5, #vf_coef=0.5,
use_sde=use_sde, # False # use_sde=use_sde, # False
sde_sample_freq=8, # sde_sample_freq=8,
#clip_range=None # 1 # 0.2, #clip_range=None # 1 # 0.2,
)
# trl_frob = PPO(
# MlpPolicy,
# env,
# projection=FrobeniusProjectionLayer(),
# verbose=0,
# tensorboard_log=root_path+"/logs_tb/"+env_name +
# "/trl_frob"+(['', '_sde'][use_sde])+"/",
# learning_rate=3e-4,
# gamma=0.99,
# gae_lambda=0.95,
# normalize_advantage=True,
# ent_coef=0.03, # 0.1
# vf_coef=0.5,
# use_sde=use_sde,
# clip_range=2, # 0.2
#) #)
trl_frob = PPO(
MlpPolicyPPO,
env,
projection=FrobeniusProjectionLayer(),
verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name +
"/trl_frob"+(['', '_sde'][use_sde])+"/",
learning_rate=3e-4,
gamma=0.99,
gae_lambda=0.95,
normalize_advantage=True,
ent_coef=0.03, # 0.1
vf_coef=0.5,
use_sde=use_sde,
clip_range=2, # 0.2
)
print('SAC:') #print('SAC:')
testModel(sac, timesteps, showRes, #testModel(sac, timesteps, showRes,
saveModel, n_eval_episodes)
# print('TRL_frob:')
# testModel(trl_frob, timesteps, showRes,
# saveModel, n_eval_episodes) # saveModel, n_eval_episodes)
print('TRL_frob:')
testModel(trl_frob, timesteps, showRes,
saveModel, n_eval_episodes)
def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, saveModel=True, n_eval_episodes=4): def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, saveModel=True, n_eval_episodes=4):