deepcomp 1.4.2

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

deepcomp 1.4.1

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

deepcomp 1.4.0

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: v1.3.0...v1.4.0

deepcomp 1.3.0

• 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

deepcomp 1.2.5

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

deepcomp 1.2.4

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

deepcomp 1.2.3

Another small fix in the Readme

deepcomp 1.2.2

Minor fix in readme for PyPi

deepcomp 1.2.1

Minor fixes in Readme and rendered video.

deepcomp 1.2.0

• Much improved and extended render function for nicer visualization. New --dashboard mode, new icons, metrics, etcs.
• New, restructured CLI args
• Clean up issues (with DeepSource)
• Minor other changes