Reinforcement Learning
A machine learning paradigm where agents learn by interacting with an environment, receiving rewards or penalties for actions. Used in robotics, games, and optimisation.
In-Depth Explanation
Reinforcement learning (RL) trains agents to make sequential decisions by learning from experience. Unlike supervised learning, there's no labelled dataset - the agent learns through trial and error.
Core RL concepts:
- Agent: The learner/decision maker
- Environment: What the agent interacts with
- State: Current situation
- Action: What the agent can do
- Reward: Feedback signal (positive or negative)
- Policy: Strategy for choosing actions
Key algorithms:
- Q-Learning: Learning action values for states
- Policy Gradient: Directly learning the action policy
- Actor-Critic: Combining value and policy methods
- PPO/TRPO: Stable policy optimisation
RL applications:
- Game playing (AlphaGo, game AI)
- Robotics and control
- Recommendation systems
- Resource allocation
- RLHF for LLM alignment
Business Context
Reinforcement learning powers dynamic pricing, recommendation engines, and resource optimisation. RLHF is how modern LLMs like ChatGPT are aligned to be helpful.
How Clever Ops Uses This
We implement RL-based solutions for Australian businesses in optimisation and decision-making scenarios where traditional approaches fall short.
Example Use Case
"Training an AI to optimise warehouse robot paths, learning efficient routes through trial and error in simulated environments."