add training code

Author: ofirnachum

research/efficient-hrl/README.md

@@ -1,28 +1,62 @@
-Code for performing Hierarchical RL based on
+Code for performing Hierarchical RL based on the following publications:
+
 "Data-Efficient Hierarchical Reinforcement Learning" by
 Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, and Sergey Levine
 (https://arxiv.org/abs/1805.08296).
 
-
-This library currently includes three of the environments used:
-Ant Maze, Ant Push, and Ant Fall.
-
-The training code is planned to be open-sourced at a later time.
+"Near-Optimal Representation Learning for Hierarchical Reinforcement Learning"
+by Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, and Sergey Levine
+(https://arxiv.org/abs/1810.01257).
 
 
 Requirements:
 * TensorFlow (see http://www.tensorflow.org for how to install/upgrade)
+* Gin Config (see https://github.com/google/gin-config)
+* Tensorflow Agents (see https://github.com/tensorflow/agents)
 * OpenAI Gym (see http://gym.openai.com/docs, be sure to install MuJoCo as well)
 * NumPy (see http://www.numpy.org/)
 
 
 Quick Start:
 
-Run a random policy on AntMaze (or AntPush, AntFall):
+Run a training job based on the original HIRO paper on Ant Maze:
+
+```
+python scripts/local_train.py test1 hiro_orig ant_maze base_uvf suite
+```
+
+Run a continuous evaluation job for that experiment:
 
 ```
-python environments/__init__.py --env=AntMaze
+python scripts/local_eval.py test1 hiro_orig ant_maze base_uvf suite
 ```
 
+To run the same experiment with online representation learning (the
+"Near-Optimal" paper), change `hiro_orig` to `hiro_repr`.
+You can also run with `hiro_xy` to run the same experiment with HIRO on only the
+xy coordinates of the agent.
+
+To run on other environments, change `ant_maze` to something else; e.g.,
+`ant_push_multi`, `ant_fall_multi`, etc.  See `context/configs/*` for other options.
+
+
+Basic Code Guide:
+
+The code for training resides in train.py.  The code trains a lower-level policy
+(a UVF agent in the code) and a higher-level policy (a MetaAgent in the code)
+concurrently.  The higher-level policy communicates goals to the lower-level
+policy.  In the code, this is called a context.  Not only does the lower-level
+policy act with respect to a context (a higher-level specified goal), but the
+higher-level policy also acts with respect to an environment-specified context
+(corresponding to the navigation target location associated with the task).
+Therefore, in `context/configs/*` you will find both specifications for task setup
+as well as goal configurations.  Most remaining hyperparameters used for
+training/evaluation may be found in `configs/*`.
+
+NOTE: Not all the code corresponding to the "Near-Optimal" paper is included.
+Namely, changes to low-level policy training proposed in the paper (discounting
+and auxiliary rewards) are not implemented here.  Performance should not change
+significantly.
+
 
 Maintained by Ofir Nachum (ofirnachum).