add more to README

README.md

@@ -5,10 +5,18 @@ The goal of the library is to provide the necessary components for quickly build
 as well as providing high-quality reference implementations of modern DRL algorithms.
 The full documentation can be found at the following URL: [https://autonomous-learning-library.readthedocs.io](https://autonomous-learning-library.readthedocs.io).
 
-## Building New Agents
+## Tools for Building New Agents
 
-The primary goal of the `autonomous-learning-library` is to facilitate the rapid development of new reinforcement learning agents by providing common functionality out-of-the-box.
-See the [Getting Started](https://autonomous-learning-library.readthedocs.io) guide for a full description of the functionality provided by the `autonomous-learning-library`.
+The primary goal of the `autonomous-learning-library` is to facilitate the rapid development of new reinforcement learning agents by providing common tools for building and evaluation agents, such as:
+
+    * A flexible function `Approximation` API that integrates features such as target networks, gradient clipping, learning rate schedules, model checkpointing, multi-headed networks, loss scaling, logging, and more.
+    * Various memory buffers, including prioritized experience replay (PER), generalized advantage estimation (GAE), and more.
+    * A `torch`-based `Environment` interface that simplies agent implementations by cutting out the `numpy` middleman.
+    * Common wrappers and agent enhancements for replicating standard benchmarks.
+    * [Slurm](https://slurm.schedmd.com/documentation.html) integration for running large-scale experiments.
+    * Plotting and logging utilities including `tensorboard` integration and utilities for generating common plots.
+
+See the [documentation](https://autonomous-learning-library.readthedocs.io) guide for a full description of the functionality provided by the `autonomous-learning-library`.
 Additionally, we provide an [example project](https://github.com/cpnota/all-example-project) which demonstrates the best practices for building new agents.
 
 ## High-Quality Reference Implementations