DRL Robot navigation in IR-SIM
Deep Reinforcement Learning algorithm implementation for simulated robot navigation in IR-SIM. Using 2D laser sensor data and information about the goal point a robot learns to navigate to a specified point in the environment.
Installation
- Package versioning is managed with poetry
pip install poetry - Clone the repository
git clone https://github.com/reiniscimurs/DRL-robot-navigation.git - Navigate to the cloned location and install using poetry
poetry install
Training the model
-
Run the training by executing the train.py file
poetry run python robot_nav/train.py -
To open tensorbord, in a new terminal execute
tensorboard --logdir runs
Sources
| Package/Model | Description | Model Source |
|---|---|---|
| IR-SIM | Light-weight robot simulator | https://github.com/hanruihua/ir-sim |
| TD3 | Twin Delayed Deep Deterministic Policy Gradient model | https://github.com/reiniscimurs/DRL-Robot-Navigation-ROS2 |
| SAC | Soft Actor-Critic model | https://github.com/denisyarats/pytorch_sac |
| PPO | Proximal Policy Optimization model | https://github.com/nikhilbarhate99/PPO-PyTorch |
| DDPG | Deep Deterministic Policy Gradient model | Updated from TD3 |
| CNNTD3 | TD3 model with 1D CNN encoding of laser state | - |
| RCPG | Recurrent Convolution Policy Gradient - adding recurrence layers (lstm/gru/rnn) to CNNTD3 model | - |