Reinforcement Learning: a gentle Introduction and industrial Application | Dr. Christian Hidber

• Reinforcement learning separates the policy from the learner, allowing for complex decision-making.
• Implementing a policy requires initializing weights with random values and interacting with an environment.
• Policy update involves calculating rewards, updating weights, and choosing the next action.
• A neural network can be used to approximate the policy and value function.
• Reinforcement learning can be applied to complex problems like hydrating systems, where a simulated game is created and the policy is learned through trial and error.
• Traditional machine learning approaches were not effective for this problem, but reinforcement learning achieved a success rate of 93%.
• The game state is represented as a vector, and the actions are encoded as a matrix or image.
• Policy gradient algorithms, such as ProPlanner, can be used for this type of problem.
• The learning rate can be adjusted to influence the pace of learning.
• The reward function is critical in shaping the policy and guiding the agent’s behavior.
• The reward function is designed to encourage the agent to win the game and avoid errors.
• Softmax is used to convert the values to probabilities.
• The child (learning algorithm) tries to avoid taking the same action again to reduce its probability.
• The policy is updated after each game, and the neural network is used to store and retrieve the knowledge.
• The policy is chosen by taking the action with the highest probability in each state.
• The algorithm iterates through multiple games, updating the policy and weights after each game.
• The For loop in the algorithm iterates through each step of the game, updating the total reward and policy.
• The algorithm uses a combination of knowledge from the experts and random exploration to learn the optimal policy.
• The learning rate can be adjusted to balance exploration and exploitation.

Note: These points are summarized from the provided transcript and may not be exhaustive or verbatim.