[docs] Add POCA to major features

README.md

@@ -29,11 +29,9 @@ developer communities.
 - 18+ [example Unity environments](docs/Learning-Environment-Examples.md)
 - Support for multiple environment configurations and training scenarios
 - Flexible Unity SDK that can be integrated into your game or custom Unity scene
-- Training using two deep reinforcement learning algorithms, Proximal Policy
-  Optimization (PPO) and Soft Actor-Critic (SAC)
-- Built-in support for Imitation Learning through Behavioral Cloning (BC) or
-  Generative Adversarial Imitation Learning (GAIL)
-- Self-play mechanism for training agents in adversarial scenarios
+- Support for training single-agent, multi-agent cooperative, and multi-agent
+  competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).
+- Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).
 - Easily definable Curriculum Learning scenarios for complex tasks
 - Train robust agents using environment randomization
 - Flexible agent control with On Demand Decision Making

docs/ML-Agents-Overview.md

@@ -20,7 +20,8 @@
     - [Recording Demonstrations](#recording-demonstrations)
   - [Summary](#summary)
 - [Training Methods: Environment-specific](#training-methods-environment-specific)
-  - [Training in Multi-Agent Environments with Self-Play](#training-in-multi-agent-environments-with-self-play)
+  - [Training in Competitive Multi-Agent Environments with Self-Play](#training-in-competitive-multi-agent-environments-with-self-play)
+  - [Training in Cooperative Multi-Agent Environments with MA-POCA](#training-in-cooperative-multi-agent-environments-with-ma-poca)
   - [Solving Complex Tasks using Curriculum Learning](#solving-complex-tasks-using-curriculum-learning)
   - [Training Robust Agents using Environment Parameter Randomization](#training-robust-agents-using-environment-parameter-randomization)
 - [Model Types](#model-types)