fix(inference): refine scaling and rerouting rules and action behavior

fix(inference): add debugging and error handling for env.step action calls

perf(environment): improve cost model with three-tier capacity pricing

refactor(simulator): add feedback on action effects and enforce capacity discipline

better task 3

fixes

Merge branch 'integrate/k8s-tennodes' of https://github.com/PranavKK1201/Anti-Atropos into integrate/k8s-tennodes

reward tuning

graph modelling

fixed envs

fixed

str

feat: implement Kubernetes executor for automated cluster scaling and infrastructure management

upgraded to 10 nodes

feat(curriculum): add progressive training curriculum management

ui and inference changes

feat: implement AntiAtroposEnvironment for SRE simulation and add Kubernetes integration tests

Add reward handling and normalization features to environment

modified tasks for compatibility and added the inference.py script

feat: Add observability setup guide and integrate Prometheus and Grafana

basic of grafan, kuber, and prometheus added

added VIP nodes, which have a higher weightage

fixed task 1, 2, 3

feat: implement core SRE simulation environment, Pydantic schemas, and physics models for task-based cluster management

kinda almost fixed task 1

normalized, better grading and env

tmp

update cost model and observation descriptions for clarity and accuracy

added dropout, probability, normalization, and limits to values. Making the environment more challenging and balanced

integrate production simulator, stability math, and reward recalibration

renamed