Training Specifications
=======================

Specify the following parameters in the ``./configs/configs.txt`` file for specification of the training algorithm.

**model**

The neural network model to use in the muSim controller, can be ['rnn', 'gru']

**hidden_size**

The number of hidden units in the RNN/Feedforward layers of the muSim controller

**mode**

The mode of simulation can be [train, test, SFE, sensory_pert, neural_pert, musculo_properties]

Use 'train' for training and 'test' for testing the trained controller

(Remaining parameters are discussed in the perturbation modules section).

**RL_algorithm = 'SAC'**

The RL algorithm can be either [SAC, DDPG, TD3] (Standard DRL algorithms)

**cuda = True/False**

Utilize GPU for training.

**Other DRL specific parameters**

**Default values are recommended as they lead to succesfull training**

gamma = 0.99

tau = 0.005

lr = 0.0003

alpha = 0.20

automatic_entropy_tuning = True

seed = 123456

policy_batch_size = 8

policy_replay_size = 4000

multi_policy_loss = True

batch_iters = 1

total_episodes = 1000000

condition_selection_strategy = "reward"


**For the TD3 algorithm, the following parameters must be specified**

target_noise = .2

target_noise_clip = .5

policy_delay = 2