# RL-AFEC

**Repository Path**: lpc-v/RL-AFEC

## Basic Information

- **Project Name**: RL-AFEC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-19
- **Last Updated**: 2023-10-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# RL-AFEC: Adaptive Forward Error Correction for Real-time Video Communication Based on Reinforcement Learning
This is a Tensorflow implementation of RL-AFEC.
# Prerequisites
Python 3.6.8, Tensorflow v2.2.0 (CPU version)

Modules: Numpy, tqdm, absl-py

# Checklist
1. Specify num_agent, the number of worker threads collecting experience each run, using FLAG in train.py. For example, if your CPU has 12 cores, you can set num_agent to 11.
2. Return values of the reward function in game.py for training and testing are different. Please change one line of code when you switch from training to testing and vice versa. Please see below for more details.

# Training
To train an RL-AFEC agent, put the trace file (e.g., video_RL_train.txt) in data/, then specify the file name in config.py, i.e., trace_file = 'video_RL_train.txt' 

In game.py, let function reward() return r:
```python
def reward(self, tm_idx, actions):    
        ...
        return r
        #return vmaf, bw_waste, loss_pattern_after #for testing
```
then run
```python
python3 train.py
```
# Testing
To test the trained policy on a set of test traces, put the test trace file (e.g., video_RL_testing.txt) in data/, then specify the file name in config.py, i.e., test_trace_file = 'video_RL_testing.txt'

In game.py, let function reward() return vmaf, bw_waste, loss_pattern_after:
```python
def reward(self, tm_idx, actions):    
        ...
        #return r
        return vmaf, bw_waste, loss_pattern_after #for testing
```
then run
```python
python3 test.py
```