Demonstrates training a UFACTORY X-ARM 5 robotic arm using reinforcement learning with AMD Schola v2.0 and Unreal Engine. The tutorial progresses through three increasingly complex tasks: reaching a fixed target, reaching randomized target positions, and handling both random targets and locations. Uses the SAC algorithm from Stable-Baselines3 with continuous observation and action spaces, showing how to build robust models that adapt to dynamic conditions through progressive complexity.

5m read timeFrom gpuopen.com
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Robot armStructure of the environment & tasksTask 1 - reach with fixed locationTask 2 - reach with random locationTask 3 - random target, random locationConclusionRunning with AMD Schola

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