<gh_stars>0
ACTION_DISCRETISATION = 5
ACTION_NOISE = 0.1
BACKTRACK_COEFF = 0.8
BACKTRACK_ITERS = 10
CONJUGATE_GRADIENT_ITERS = 10
DAMPING_COEFF = 0.1
DISCOUNT = 0.99
EPSILON = 0.05
ENTROPY_WEIGHT = 0.2
HIDDEN_SIZE = 64
KL_LIMIT = 0.05
LEARNING_RATE = 0.001
MAX_STEPS = 2*10**6
ON_POLICY_BATCH_SIZE = 2048
OFF_POLICY_BATCH_SIZE = 128
POLICY_DELAY = 2
POLYAK_FACTOR = 0.995
PPO_CLIP_RATIO = 0.2
PPO_EPOCHS = 20
REPLAY_SIZE = 100000
TARGET_ACTION_NOISE = 0.2
TARGET_ACTION_NOISE_CLIP = 0.5
TARGET_UPDATE_INTERVAL = 2500
TRACE_DECAY = 0.97
UPDATE_INTERVAL = 1
UPDATE_START = 10000
TEST_INTERVAL = 2000


import torch

# MULTIPROCESSING = False
MULTIPROCESSING = True

#Avoid a bug in torch that throws an error if multiprocessing is used after any call to cuda
if MULTIPROCESSING:
  CUDA = False
else:
  CUDA = torch.cuda.is_available()
# CUDA = False
DEVICE=torch.device('cuda' if CUDA else 'cpu')

ACTION_DISCRETISATION = 5
ACTION_NOISE = 0.1
BACKTRACK_COEFF = 0.8
BACKTRACK_ITERS = 10
CONJUGATE_GRADIENT_ITERS = 10
DAMPING_COEFF = 0.1
DISCOUNT = 0.99
EPSILON = 0.05
ENTROPY_WEIGHT = 0.2
HIDDEN_SIZE = 64
KL_LIMIT = 0.05
LEARNING_RATE = 0.001
MAX_STEPS = 2*10**6
ON_POLICY_BATCH_SIZE = 2048
OFF_POLICY_BATCH_SIZE = 128
POLICY_DELAY = 2
POLYAK_FACTOR = 0.995
PPO_CLIP_RATIO = 0.2
PPO_EPOCHS = 20
REPLAY_SIZE = 100000
TARGET_ACTION_NOISE = 0.2
TARGET_ACTION_NOISE_CLIP = 0.5
TARGET_UPDATE_INTERVAL = 2500
TRACE_DECAY = 0.97
UPDATE_INTERVAL = 1
UPDATE_START = 10000
TEST_INTERVAL = 2000


import torch

# MULTIPROCESSING = False
MULTIPROCESSING = True

#Avoid a bug in torch that throws an error if multiprocessing is used after any call to cuda
if MULTIPROCESSING:
  CUDA = False
else:
  CUDA = torch.cuda.is_available()
# CUDA = False
DEVICE=torch.device('cuda' if CUDA else 'cpu')
# MULTIPROCESSING = False #Avoid a bug in torch that throws an error if multiprocessing is used after any call to cuda # CUDA = False