hyperparameters.py

import torch.nn.functional as F

# Default hyperparameters
                           
SEED = 10                          # Random seed

NB_EPISODES = 10000                # Max nb of episodes
NB_STEPS = 1000                    # Max nb of steps per episodes 
UPDATE_EVERY_NB_EPISODE = 4        # Nb of episodes between learning process
MULTIPLE_LEARN_PER_UPDATE = 3      # Nb of multiple learning process performed in a row

BUFFER_SIZE = int(1e5)             # replay buffer size
BATCH_SIZE = 200                   # minibatch size

ACTOR_FC1_UNITS = 400  #256        # Number of units for the layer 1 in the actor model
ACTOR_FC2_UNITS = 300  #128        # Number of units for the layer 2 in the actor model
CRITIC_FCS1_UNITS = 400  #256      # Number of units for the layer 1 in the critic model
CRITIC_FC2_UNITS = 300  #128       # Number of units for the layer 2 in the critic model
NON_LIN = F.relu   #F.leaky_relu   # Non linearity operator used in the model
LR_ACTOR = 1e-4    #1e-4           # learning rate of the actor 
LR_CRITIC = 5e-3   #2e-3           # learning rate of the critic
WEIGHT_DECAY = 0   #0.0001         # L2 weight decay

GAMMA = 0.995 #0.99                # Discount factor
TAU = 1e-3                         # For soft update of target parameters
CLIP_CRITIC_GRADIENT = False       # Clip gradient during Critic optimization

ADD_OU_NOISE = True                # Add Ornstein-Uhlenbeck noise
MU = 0.                            # Ornstein-Uhlenbeck noise parameter
THETA = 0.15                       # Ornstein-Uhlenbeck noise parameter
SIGMA = 0.2                        # Ornstein-Uhlenbeck noise parameter
NOISE = 1.0                        # Initial Noise Amplitude 
NOISE_REDUCTION = 1.0 # 0.995      # Noise amplitude decay ratio