artlib.reinforcement
Reinforcement learning (RL) is a type of machine learning where agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The SARSA (State-Action-Reward-State-Action) algorithm is an on- policy RL method that updates the agent’s policy based on the action actually taken. This contrasts with Q-learning, which is off-policy and learns the optimal action independently of the agent’s current policy.
Reactive learning, on the other hand, is a more straightforward approach where decisions are made solely based on immediate observations, without the complex state-action-reward feedback loop typical of RL models like SARSA or Q-learning. It lacks the depth of planning and long-term reward optimization seen in traditional RL.
The modules herein only provide for reactive and SARSA style learning.