Deepcomp

DeepCoMP is now accepted for publication in the 2023 IEEE Transaction on Network and Service Management (TNSM) as "Multi-Agent Deep Reinforcement Learning for Coordinated Multipoint in Mobile Networks" 🎉

* Updated Readme
* Fix dependencies for correct installation: Pin `protobuf` and `pydantic`

* Enable `'avg'` reward aggregation for DeepCoMP by default (was `sum`)
* Add `--debug` CLI option for running in a debugger

Improvements regarding utility functions:
* Use constants to define the max and min utility, which are then applied for normalization, reward clipping, and rendering
* Support two additional utility functions (in addition to log): Linear (ie, just data rate) and step function.
* Configurable via CLI. But: Requires manual adjustment of MIN_UTILITY and MAX_UTILITY


**Full Changelog**: https://github.com/CN-UPB/DeepCoMP/compare/v1.3.0...v1.4.0

* Two, configurable heuristics: Dynamic and static
* Configurable dynamic UE arrival and departure over time
* Changed reward function for multi-agent: Weighted avg. QoE over all cells in range (based on their connected UEs)
* Added observation for multi-agent: Avg. QoE of connected UEs at each cell
* Multiple smaller changes, fixes, eg, upgrade to Ray 1.4

Updated Docker support: https://hub.docker.com/r/stefanbschneider/deepcomp

* Critical bug fix in CLI
* Improved formatting
* Docker support