https://github.com/shandilya1998/neurorobotics
Python tools for robotics, deep reinforcement learning and neuroscience research.
https://github.com/shandilya1998/neurorobotics
computational-neuroscience mujoco mujoco-py numpy openai-gym pytorch reinforcement-learning robotics
Last synced: 2 months ago
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Python tools for robotics, deep reinforcement learning and neuroscience research.
- Host: GitHub
- URL: https://github.com/shandilya1998/neurorobotics
- Owner: shandilya1998
- Created: 2021-09-17T19:04:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-10T03:19:53.000Z (about 3 years ago)
- Last Synced: 2025-03-22T23:25:29.223Z (over 1 year ago)
- Topics: computational-neuroscience, mujoco, mujoco-py, numpy, openai-gym, pytorch, reinforcement-learning, robotics
- Language: Python
- Homepage:
- Size: 420 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# NavigationController
## Spawn Test Environment
```
from simulations.maze_env import MazeEnv
from simulations.point import PointEnv
from simulations.maze_task import CustomGoalReward4Rooms
env = MazeEnv(PointEnv, CustomRewardGoal4Rooms)
```
## Experiment Checklist
- Check and update `def step(**kwargs)` and `def _set_action_space(**kwargs)` in [simulations/point.py](simulations/point.py)
- Check and update `def _get_obs(**kwargs)` and `def _set_observation_space(**kwargs)` in [simulations/maze_env.py](simulations/maze_env.py) and [simulations/point.py](simulations/point.py)
- Update Reward and Info Computation Pipeline
- Check and update `class Actor` and `class Critic` in [utils/rtd3_utils.py](utils/rtd3_utils.py)
- Check and update `class Autoencoder`, `class VisualCortexV4`, `class EncoderBody` and `class FeatureExtractionBackbone` in [bg/models.py](bg/models.py)
- Check and update `def update_policy(**kwargs)` and `def _sample_action(**kwargs)` in [utils/rtd3_utils.py](utils/rtd3_utils.py)
- Check and update `def predict(**kwargs)` in [utils/rtd3_utils.py](utils/rtd3_utils.py)
- Ensure `squash_output` is False in `class Actor` if using inverted gradients method
- Check and update `seed`, `batch_size`, `learning_starts`, `imitation_steps` and `debug` in `params` from [constant.py](constants.py)
- Check and update other hyperparameters for tuning
- Set `LOGDIR`, `ENV_TYPE`, `TIMESTEPS`, `MAX_EPISODE_SIZE`, `HISTORY_STEPS` and `TASK_VERSION` in [train.sh](train.sh)
- Check and update `net_arch` and `n_critics` in [learning/explore.py](learning/explore.py)
- Run `sh train.sh` in terminal for debugging. Run `nohup sh train.sh >> assets/out/models/train.log &` in terminal for GPU execution.
- Commit and Push to Github. Update local `experiments_log`
## Installation on docker
- Run `docker build -t neuroengineering-tools ./docker/cpu/` in the root folder of the repository.
- Run `docker run -dit --name sample neuroengineering-tools` to spawn a new container.
- Run desired command with the tools in the container.
- Run `docker stop neuroengineering-tools` to stop running container.