State Action Reward State Action in 60 Seconds | Machine Learning Algorithms
📺 State Action Reward State Action in 60 Seconds | Machine Learning Algorithms
📖 The Hitchhiker's Guide to Machine Learning Algorithms | by @serpdotai
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Imagine you're a baby learning to walk. You take a step forward and feel the ground beneath your feet. That's the state. You take another step and feel your balance starting to shift. That's the action. You stagger forward, but manage to stay on your feet. That's the reward.
The next time you try to take a step, your brain remembers that last reward and adjusts your actions accordingly. That's the state-action-reward-state- action algorithm, also known as SARSA.
In simpler terms, SARSA is a way for machines to learn from their actions and adjust their behavior based on the feedback they receive. It's often used in reinforcement learning, where an agent interacts with an environment and receives rewards or punishments based on its actions. By training a Markov decision process model on a new policy, SARSA helps the machine make more informed decisions in the future.
With SARSA, machines can "learn" like a baby learning to walk, taking steps forward and adjusting based on the feedback they receive. It's a powerful tool in the world of artificial intelligence and machine learning that helps agents make smarter decisions and achieve better outcomes.
The State-Action-Reward-State-Action (SARSA) algorithm is an on-policy reinforcement learning algorithm used to train a Markov decision process model based on a new policy. SARSA is a type of temporal difference learning method that updates its Q-values based on the current state, action, reward, next state, and next action. Unlike some other reinforcement learning algorithms, SARSA takes into account the current policy being pursued when updating its Q-values, making it particularly useful for problems where the agent cannot completely explore the state-action space.
Because it is an on-policy algorithm, SARSA learns by interacting with the environment using the same policy that it is improving. This means that it may take longer to converge on an optimal policy than off-policy algorithms like Q-learning, but it has the advantage of being more stable and able to handle stochastic environments. SARSA is commonly used in problems with discrete state and action spaces, such as gridworld and cartpole simulations, and has also been adapted for continuous state and action spaces.
As a reinforcement learning algorithm, SARSA is particularly useful for tasks where feedback is delayed or sparse, such as playing a game of chess or controlling a robot. By gradually updating its Q-values based on the reward received for each action taken, SARSA can learn to make better decisions over time and ultimately arrive at an optimal policy for the given task.
With its flexibility and robustness, the SARSA algorithm has become an essential tool in the field of artificial intelligence and machine learning, allowing engineers to create intelligent systems that can learn and adapt to new challenges and environments.
State-Action-Reward-State-Action: Use Cases & Examples
SARSA is an on-policy algorithm that is commonly used in reinforcement learning to train a Markov decision process model on a new policy. It falls under the category of temporal difference learning methods, which is a type of machine learning that learns from experience and adjusts its predictions based on the difference between predicted and actual outcomes.
One of the most notable use cases of SARSA is in robotic control. For example, SARSA can be used to teach a robot to navigate a maze by providing it with a reward for reaching the end and penalizing it for hitting a wall. The robot uses SARSA to learn the optimal path to take through the maze based on its current state and the actions it takes.
Another use case of SARSA is in game playing. SARSA can be used to train an agent to play a game by rewarding it for winning and penalizing it for losing. The agent learns the optimal actions to take based on the current state of the game and the actions it takes.
Furthermore, SARSA has been used in autonomous vehicle control. The algorithm can be used to teach a self-driving car to navigate through traffic by providing it with a reward for reaching its destination and penalizing it for causing an accident. SARSA allows the car to learn from its experiences and make better decisions in the future.
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