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Rough Draft of MADDPG
Code isn't ready to commit to my branch but wanted to start sharing it:
@wyatt522, basically I got an outline of what I believe are all the needed functions.
Working on:
- creating functions to modify parameters for each agent after every iteration
- passing correct variables and values between functions
- having MADDPG output compatible data for the main MultiAgentEnvironment.py file to plot
Separate Files Please
Please organize classes into separate files
Please
Multi Agent Environment Needs Fixing
Single UAV works, issue is that multiple agent sim throws errors:
- A* might be getting confused
- Or path might not be clearly returned
- Need to add some kind of test to see what is actually happening
If Multi-agent sim doesn't work by EOD 7/2/23, transition to single UAV sim for phase 2 progress report
User inputs
RunSim.py user input for scenario selection is convenient.
Let's expand this feature to ask the user for the desired number of swarm agents.
Simplified GWO needs continued development
Tabatha to:
- initialize search space
- update fitness equation
- develop hierarchy function
- implement GWO search loop with wolf population initialization, fitness + search iterative loop, path reconstruction
make a pretty readme
Missing evaluation methods
Add the following evaluation methods:
- check ability to generate successful flight path in all scenarios (3 obstacle configs x 3 swarm sizes)
- completeness
- execution time, time complexity
- space complexity
- optimality (cost)
- convergence effect
- statistical results -> optimal, mean, worst values + std deviation (20 runs per scenario)
- scalability (compare results from 1-, 3-, 5-agent swarms)
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