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Update README.md

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Gal Leibovich
2018-08-13 17:19:47 +03:00
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@@ -111,20 +111,20 @@ To list all the available presets use the `-l` flag.
To run a preset, use:
```bash
python3 coach.py -r -p <preset_name>
python3 rl_coach/coach.py -r -p <preset_name>
```
For example:
* CartPole environment using Policy Gradients (PG):
```bash
python3 coach.py -r -p CartPole_PG
python3 rl_coach/coach.py -r -p CartPole_PG
```
* Basic level of Doom using Dueling network and Double DQN (DDQN) algorithm:
```bash
python3 coach.py -r -p Doom_Basic_Dueling_DDQN
python3 rl_coach/coach.py -r -p Doom_Basic_Dueling_DDQN
```
Some presets apply to a group of environment levels, like the entire Atari or Mujoco suites for example.
@@ -136,7 +136,7 @@ For example:
* Pong using the Nerual Episodic Control (NEC) algorithm:
```bash
python3 coach.py -r -p Atari_NEC -lvl pong
python3 rl_coach/coach.py -r -p Atari_NEC -lvl pong
```
There are several types of agents that can benefit from running them in a distrbitued fashion with multiple workers in parallel. Each worker interacts with its own copy of the environment but updates a shared network, which improves the data collection speed and the stability of the learning process.
@@ -146,7 +146,7 @@ For example:
* Breakout using Asynchronous Advantage Actor-Critic (A3C) with 8 workers:
```bash
python3 coach.py -r -p Atari_A3C -lvl breakout -n 8
python3 rl_coach/coach.py -r -p Atari_A3C -lvl breakout -n 8
```