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Use this resource to design environments and train agents with reinforcement learning
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In a terminal, from the folder you want:
npx --yes palatekit@latest install reinforcement-learning-subagentFirst time: connect the CLI with
npx --yes palatekit@latest loginThe reinforcement-learning-engineer subagent is called to define state and action spaces, design the reward, choose an algorithm (DQN, PPO, SAC, TD3, among others), train the policy and deploy it while keeping safety constraints.
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Updated November 2, 2026
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“Design the reward function for an agent that manages a simulated stock.”

Subagent published by VoltAgent under the MIT license, delivered in its original version.
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« reinforcement-learning-engineer » (VoltAgent, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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