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Update README with new coach aliases

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Itai Caspi
2018-08-14 14:36:41 +03:00
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parent 7a76d63da4
commit 824fdeee59
1 changed files with 7 additions and 7 deletions
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README.md
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@@ -14,7 +14,7 @@ Basic RL components (algorithms, environments, neural network architectures, exp
Training an agent to solve an environment is as easy as running: Training an agent to solve an environment is as easy as running:
```bash ```bash
python3 coach.py -p CartPole_DQN -r coach -p CartPole_DQN -r
``` ```
<img src="img/doom_deathmatch.gif" alt="Doom Deathmatch" width="267" height="200"/> <img src="img/carla.gif" alt="CARLA" width="284" height="200"/> <img src="img/montezuma.gif" alt="MontezumaRevenge" width="152" height="200"/> <img src="img/doom_deathmatch.gif" alt="Doom Deathmatch" width="267" height="200"/> <img src="img/carla.gif" alt="CARLA" width="284" height="200"/> <img src="img/montezuma.gif" alt="MontezumaRevenge" width="152" height="200"/>
@@ -111,20 +111,20 @@ To list all the available presets use the `-l` flag.
To run a preset, use: To run a preset, use:
```bash ```bash
python3 rl_coach/coach.py -r -p <preset_name> coach -r -p <preset_name>
``` ```
For example: For example:
* CartPole environment using Policy Gradients (PG): * CartPole environment using Policy Gradients (PG):
```bash ```bash
python3 rl_coach/coach.py -r -p CartPole_PG coach -r -p CartPole_PG
``` ```
* Basic level of Doom using Dueling network and Double DQN (DDQN) algorithm: * Basic level of Doom using Dueling network and Double DQN (DDQN) algorithm:
```bash ```bash
python3 rl_coach/coach.py -r -p Doom_Basic_Dueling_DDQN coach -r -p Doom_Basic_Dueling_DDQN
``` ```
Some presets apply to a group of environment levels, like the entire Atari or Mujoco suites for example. 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: * Pong using the Nerual Episodic Control (NEC) algorithm:
```bash ```bash
python3 rl_coach/coach.py -r -p Atari_NEC -lvl pong coach -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. 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: * Breakout using Asynchronous Advantage Actor-Critic (A3C) with 8 workers:
```bash ```bash
python3 rl_coach/coach.py -r -p Atari_A3C -lvl breakout -n 8 coach -r -p Atari_A3C -lvl breakout -n 8
``` ```
@@ -164,7 +164,7 @@ While Coach trains an agent, a csv file containing the relevant training signals
To use it, run: To use it, run:
```bash ```bash
python3 dashboard.py dashboard
``` ```