Implement finger inverse kinematic with neural network and reinforcement learning algorithm ddpg.
python3.6
numpytensorflowopencv-python
Use --help to see usage of train.py and test.py.
usage: train.py [-h] [--model_type MODEL_TYPE] [--regress_path REGRESS_PATH]
[--rl_path RL_PATH]
optional arguments:
-h, --help show this help message and exit
--model_type MODEL_TYPE
model type index, 0 for regression model and 1 for
reinforcement learning model, default 0
--regress_path REGRESS_PATH
path to save regression model weight file, default
./models/regression_model/regression_model.ckpt
--rl_path RL_PATH path to save ddpg model weight file, default
./models/rl_model/rl_model.ckpt
usage: test.py [-h] [--model_type MODEL_TYPE] [--regress_path REGRESS_PATH]
[--rl_path RL_PATH]
optional arguments:
-h, --help show this help message and exit
--model_type MODEL_TYPE
model type index, 0 for regression model and 1 for
reinforcement learning model, default 0
--regress_path REGRESS_PATH
path to load regression model weight file, default
./models/regression_model/regression_model.ckpt
--rl_path RL_PATH path to load ddpg model weight file, default
./models/rl_model/rl_model.ckpt
cd finger_inverse_kinematic
To train regression model, run:
python train.py --model_type 0
To train ddpg model, run:
python train.py --model_type 1
cd finger_inverse_kinematic
To test regression model, run:
python test.py --model_type 0
To test ddpg model, run:
python test.py --model_type 1
The ddpg tensorflow implementation is copied from the reinforcement tutorial by MorvanZhou: https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/6-2-DDPG/