From b7966b31a51ae6858bee94e454135f4e337c4445 Mon Sep 17 00:00:00 2001 From: Carlos Hernandez Date: Fri, 18 Aug 2017 00:58:25 -0600 Subject: [PATCH] Adding links to source files --- baselines/deepq/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baselines/deepq/README.md b/baselines/deepq/README.md index aa5a9f4..4ea19d5 100644 --- a/baselines/deepq/README.md +++ b/baselines/deepq/README.md @@ -21,8 +21,8 @@ Be sure to check out the source code of [both](experiments/train_cartpole.py) [f Check out our simple agent trained with one stop shop `deepq.learn` function. -- `baselines/deepq/experiments/train_cartpole.py` - train a Cartpole agent. -- `baselines/deepq/experiments/train_pong.py` - train a Pong agent using convolutional neural networks. +- [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent. +- [baselines/deepq/experiments/train_pong.py](experiments/train_pong.py) - train a Pong agent using convolutional neural networks. In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. For both of the files listed above there are complimentary files `enjoy_cartpole.py` and `enjoy_pong.py` respectively, that load and visualize the learned policy. @@ -31,8 +31,8 @@ In particular notice that once `deepq.learn` finishes training it returns `act` ##### Check out the examples -- `baselines/deepq/experiments/custom_cartpole.py` - Cartpole training with more fine grained control over the internals of DQN algorithm. -- `baselines/deepq/experiments/atari/train.py` - more robust setup for training at scale. +- [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm. +- [baselines/deepq/experiments/atari/train.py](experiments/atari/train.py) - more robust setup for training at scale. ##### Download a pretrained Atari agent