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changing python to python3 everywhere to make the supported version of python explicit

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Gal Leibovich
2017-10-23 13:07:54 +03:00
parent 16604b11df
commit 1a09b7cec3
4 changed files with 20 additions and 20 deletions
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README.md
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@@ -10,7 +10,7 @@ Basic RL components (algorithms, environments, neural network architectures, exp
Training an agent to solve an environment is as easy as running:
```bash
python coach.py -p CartPole_DQN -r
python3 coach.py -p CartPole_DQN -r
```
<img src="img/doom.gif" alt="Doom Health Gathering" width="265" height="200"/><img src="img/minitaur.gif" alt="PyBullet Minitaur" width="265" height="200"/> <img src="img/ant.gif" alt="Gym Extensions Ant" width="250" height="200"/>
@@ -19,7 +19,7 @@ Blog post from the Intel® Nervana™ website can be found [here](https://www.in
## Installation
Note: Coach has been tested on Ubuntu 16.04 LTS only.
Note: Coach has only been tested on Ubuntu 16.04 LTS, and with Python 3.5.
Coach's installer will setup all the basics needed to get the user going with running Coach on top of [OpenAI Gym](https://github.com/openai/gym) environments. This can be done by running the following command and then following the on-screen printed instructions:
@@ -48,7 +48,7 @@ In addition to OpenAI Gym, several other environments were tested and are suppor
Coach's installer installs [Intel-Optimized TensorFlow](https://software.intel.com/en-us/articles/intel-optimized-tensorflow-wheel-now-available), which does not support GPU, by default. In order to have Coach running with GPU, a GPU supported TensorFlow version must be installed. This can be done by overriding the TensorFlow version:
```bash
pip install tensorflow-gpu
pip3 install tensorflow-gpu
```
## Running Coach
@@ -67,38 +67,38 @@ To list all the available presets use the `-l` flag.
To run a preset, use:
```bash
python coach.py -r -p <preset_name>
python3 coach.py -r -p <preset_name>
```
For example:
1. CartPole environment using Policy Gradients:
```bash
python coach.py -r -p CartPole_PG
python3 coach.py -r -p CartPole_PG
```
2. Pendulum using Clipped PPO:
```bash
python coach.py -r -p Pendulum_ClippedPPO -n 8
python3 coach.py -r -p Pendulum_ClippedPPO -n 8
```
3. MountainCar using A3C:
```bash
python coach.py -r -p MountainCar_A3C -n 8
python3 coach.py -r -p MountainCar_A3C -n 8
```
4. Doom basic level using Dueling network and Double DQN algorithm:
```bash
python coach.py -r -p Doom_Basic_Dueling_DDQN
python3 coach.py -r -p Doom_Basic_Dueling_DDQN
```
5. Doom health gathering level using Mixed Monte Carlo:
```bash
python coach.py -r -p Doom_Health_MMC
python3 coach.py -r -p Doom_Health_MMC
```
It is easy to create new presets for different levels or environments by following the same pattern as in presets.py
@@ -113,7 +113,7 @@ While Coach trains an agent, a csv file containing the relevant training signals
To use it, run:
```bash
python dashboard.py
python3 dashboard.py
```
@@ -143,7 +143,7 @@ Once a parallelized run is started, the ```train_and_sync_networks``` API will a
Then, it merely requires running Coach with the ``` -n``` flag and with the number of workers to run with. For instance, the following command will set 16 workers to work together to train a MuJoCo Hopper:
```bash
python coach.py -p Hopper_A3C -n 16
python3 coach.py -p Hopper_A3C -n 16
```