From 7a76d63da4bf4ecd3f9c2bce2b8b33b257d664ec Mon Sep 17 00:00:00 2001 From: Gal Leibovich Date: Mon, 13 Aug 2018 17:19:47 +0300 Subject: [PATCH] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index a93f7a9..4b0b4be 100644 --- a/README.md +++ b/README.md @@ -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 +python3 rl_coach/coach.py -r -p ``` 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 ```