wonmian / candy

Candy: Self-driving Agent in Carla.

Candy

Candy: Self-driving in Carla Environment.

What is candy? A model with the structure: Hierarchical Observation -- Plan&Policy -- Hierarchical Actions

We use VAE/GAN/Glow for world representation, and do RL/IL/Planning/MCTS upon it.

Demo

Performance

Car drifting (more to be uploaded):

City navigation:

VAE

Real:

Reconstructed: (With hidden state of size 50, running for 1 hour on a single GTX1080Ti)

Running Candy

(This project is still working in progress.)

• Download Carla-0.8.2 from here.

• Start CarlaUE4 engine in server mode, using commands from here.

    ./CarlaUE4.sh -windowed -ResX=800 -ResY=600 -carla-server -benchmark -fps=10
  

• Install Carla PythonClient using:

    pip install ~/carla/PythonClient
  

• Install Openai baselines under instructions here.

• Install required packages:

    pip install numpy tensorflow-gpu msgpack msgpack-numpy pyyaml tqdm gym opencv-python scipy pygame pillow
  

• Start the program by running:

    CUDA_VISIBLE_DEVICES=0 python main.py -m Town01 -l
  

• Visualization: After running the following command, open localhost:6006 on the browser.

    tensorboard -logdir=./logs
  

Candy Features

• Combining imitation learning and reinforcement learning. Candy can learn make its first turn in 40 minutes(Single GTX1080Ti) from scratch (randomize policy network).
• VAE unsupervised learning for world model construction.
• Persistent training process and flexible architecture.

Todo

• Depth, framestack as input.
• Prioritized replay for better VAE learning.
• PPO.
• Imitation learning.
• Stop when collide.
• Visualize parameter transition.
• Less summary.
• Better VAE.
• Solve catastrophic forgetting problem.
• Better RL.
• What & Where correspondence. Map data as auxilary task, using part of the hidden state.
• Distributed data collection.
• Lidar!
• Test Strategy?
• World recurrent transition model.
• Guiding commands following. (Or integrate with map)
• Traffic rules learning: Traffic Lights.
• Traffic rules learning: Signs.
• Implement MCTS.
• Auxilary tasks.
• Openai Glow?
• Speed, Depth, Orientation as inputs.
• Policy embedding? Curiosity, Attention, Memory?
• The ability of planning
• Math representation, Language acuisition
• Attentional VAE
• Attention for Representation explanation.

Ideal Features

• Curiosity-based Attention, Supervised Attention, loop-control-Attention, Interpretable Attention.
• VAE + modelbased planning + video prediction + MCTS.
• GQN, what-where in any place (Better generalization).
• guiding commands following (HRL, Multi-tasking).
• From implicit to explicit: Meta-learning, Rule Learning (Experiments from imaginary room).
• Stronger world model with enhanced VAE(maybe with attention).

Code Components

• main.py: Main file. It Deals with Carla environment.
• carla_wrapper.py: Wrap main.py, buffer information for the model.
• candy_model.py: Main model file. It is for building the big graph of the model.
• modules/* : Building blocks of the model, used in candy_model.py.