AiVibeReinforcement Learning
Reinforcement learning is a branch of machine learning in which an agent learns a policy by interacting with an environment, receiving rewards or penalties, and adjusting its actions to maximise cumulative reward over time.
Problems are usually formalised as Markov decision processes with states, actions, transition dynamics and rewards. Value-based methods such as Q-learning estimate the value of actions, policy-gradient methods such as PPO optimise the policy directly, and actor-critic methods such as SAC combine the two. Model-based methods learn a model of the environment's dynamics and use it to plan or to generate experience.
In robotics, reinforcement learning has been used for legged locomotion, dexterous in-hand manipulation, grasping and refining policies first learned from demonstrations. It is also studied for process control and scheduling problems. A related technique, reinforcement learning from human feedback, is used to align large language models with human preferences.
Learning by trial and error on real hardware is slow, costly and potentially unsafe, so robot policies are usually trained in simulation and transferred using sim-to-real techniques. Designing rewards that capture the intended behaviour without loopholes is difficult. Learned policies require extensive testing, and safety functions must remain independent of the learned controller.
Key points
- Learns from rewards obtained by interacting with an environment
- Formalised as Markov decision processes
- Common algorithms include Q-learning, PPO and SAC
- Robot policies are usually trained in simulation first
Where AiVibe comes in
AiVibe designs and manufactures the AiAmbA AI Factory, whose edge devices and AI agents let people talk to robot controllers in plain language. Robotics perception is an AiAmbA use case, and robot safety functions follow ISO 10218 and never depend on the AI layer.