Design a reinforcement learning environment with state spaces, action spaces, reward functions, and training strategies for your optimization or control problem.
## CONTEXT Reinforcement learning has achieved superhuman performance in game playing, robotics, chip design, and resource optimization — yet 80% of RL projects fail before reaching production due to poorly designed reward functions that incentivize unintended behavior, state representations that miss critical…
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