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#917 -Finished integrating all agents to either train (policy agents) or evaluate (hard-coded agents). Still some fixing up to do, tidying up, loading etc. also docs. But this is all now working.

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This commit is contained in:
Chris McCarthy
2023-06-20 16:06:55 +01:00
parent 03ae4884e0
commit a2cc4233b5
16 changed files with 1125 additions and 220 deletions
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tests/config/legacy/new_training_config.yaml
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@@ -13,7 +13,7 @@ agent_framework: RLLIB
# "A2C" (Advantage Actor Critic)
# "HARDCODED" (Custom Agent)
# "RANDOM" (Random Action)
red_agent_identifier: PPO
agent_identifier: PPO
# Sets How the Action Space is defined:
# "NODE"