Q-Learning Reinforcement Learning Algorithm

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A Q-Learning Reinforcement Learning Algorithm is a model-free off-policy RL algorithm that searches for an optimal action-selection policy for any given finite Markov decision process.



References

2022

  • https://chat.openai.com/chat
    • QUESTION: How does PPO differ from Q-learning?
    • ANSWER: Proximal Policy Optimization (PPO) and Q-learning are both reinforcement learning algorithms, but they work in quite different ways.

      Q-learning is a value-based reinforcement learning algorithm. It involves learning a value function that estimates the expected reward for an agent taking a given action in a given state. The goal of Q-learning is to find the optimal action-value function, which is the function that maximizes the expected reward for the agent. To do this, the Q-learning algorithm updates the action-value function based on the difference between the expected reward and the actual reward that the agent receives after taking an action. This process is repeated until the action-value function converges to the optimal solution.

      PPO, on the other hand, is a policy-based reinforcement learning algorithm. It involves learning a policy function that determines the actions that an agent should take in a given state. The goal of PPO is to find the optimal policy, which is the policy that maximizes the expected reward for the agent. To do this, PPO uses a combination of gradient ascent and importance sampling to update the policy function. The policy function is updated based on the difference between the expected reward and the actual reward that the agent receives after taking an action, weighted by the probability of the action under the current policy. This process is repeated until the policy function converges to the optimal solution.

      In summary, Q-learning is a value-based algorithm that learns a value function to determine the optimal action, while PPO is a policy-based algorithm that learns a policy function to determine the optimal action.

2020

  • (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/q-learning Retrieved:2020-12-10.
    • Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

      For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. "Q" names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state.


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