- pybullet
- tensorflow v2.5
- ray
- gym
$ python main.py --help
$ python main.py --train
$ python main.py --play [weight_file_path]
usage: main.py [-h] [--train] [--play PLAY]
ARS
optional arguments:
-h, --help show this help message and exit
--train Train agent with given environment
--play PLAY Play with a given weight directory
You can change config.py to fit your own flags.
hdims # dimension of hidden layers
nu # standard deviation of noise
actv = 'tanh' # activation function
out_actv = 'tanh' # activation function
# ray
n_cpu = n_workers # number of cpu
b = (n_workers//5)# number of top-performing directions to use
# Update
total_steps
ep_len_rollout
alpha # step size
# Evaluate
max_ep_len_eval
num_eval
evaluate_every
print_every