Q-Learning Reinforcement Learning Algorithm

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.

• Context:
  • It can (typically) operate by estimating the value (Action-Value Function) of taking a specific action in a given state and using this information to make optimal decisions.
  • It can (often) use a Q-Table or Q-Network for storing and updating the value estimations.
  • ...
• Example(s):
  • as used in a Video Game Playing System is used to decide the best in-game actions.
  • as used in a Robot Navigation System that finds the most efficient path in a dynamic environment.
  • as used in a Resource Management System in telecommunications for optimal resource allocation.
  • a Deep Q-Learning-based Algorithm which utilizes deep learning to manage environments with high-dimensional input spaces.
  • ...
• Counter-Example(s):
  • a Monte Carlo RL Algorithm.
  • a Sarsa RL Algorithm.
  • a PPO Algorithm.
  • Policy Gradient Methodss: Unlike Q-learning, which is a value-based method, policy gradient methods directly learn the policy function without having to learn a value function. An example is the REINFORCE Algorithm.
  • Actor-Critic Methods: These algorithms combine aspects of both policy gradient and value-based methods. They maintain both a policy model (actor) and a value model (critic), which work together to find the optimal policy. A well-known example is the Asynchronous Advantage Actor-Critic (A3C) Algorithm.
  • Trust Region Policy Optimization (TRPO): which focuses on taking the biggest possible improvement step on a policy while ensuring the new policy is not too far from the old one, differing from Q-learning's approach of updating value estimations for actions.
  • Monte Carlo Tree Search (MCTS): Used in scenarios like game playing (notably in AlphaGo), this method involves building a search tree and using random sampling of the search space, differing from Q-learning's iterative update of action values.
• See: Action-Value Function, Deep Q-Learning, Markov Decision Process, Temporal Difference Learning, Action Selection, Model-Free (Reinforcement Learning).

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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.

2020

• (Kumar et al., 2020) ⇒ Avinash Kumar, Aurick Zhou, George Tucker, et al. (2020). “Conservative Q-Learning for Offline Reinforcement Learning.” In: Advances in Neural Information Processing Systems (NeurIPS). HASH
  • NOTE: This research presents the development of a conservative Q-learning algorithm, intended for offline reinforcement learning applications. The term CQL is used to encompass both Q-learning and actor-critic methodologies.

2019

• (Spano et al., 2019) ⇒ Salvatore Spano, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, and [Additional Authors]. (2019). “An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm.” In: IEEE Transactions. HASH
  • NOTE: This paper discusses an efficient hardware implementation of the Q-Learning Algorithm, a type of reinforcement learning, emphasizing an architecture that leverages the Learning Formula in a pre-calculated manner.

2019

• (Jang et al., 2019) ⇒ Bum Jang, Min Kim, Gervais Harerimana, Jong Wook Kim. (2019). “Q-Learning Algorithms: A Comprehensive Classification and Applications.” In: IEEE Access. HASH
  • NOTE: This publication offers a comprehensive classification of Q-Learning Algorithms, detailing their evolution and the mathematical complexities involved, as well as exploring their applications within the broader context of reinforcement learning algorithms.

2016

• (Van Hasselt et al., 2016) ⇒ Hado Van Hasselt, Arthur Guez, David Silver. (2016). “Deep Reinforcement Learning with Double Q-Learning.” In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). HASH
  • NOTE: This study demonstrates that the Double Q-Learning Algorithm, originally proposed in a tabular setting, can be extended to function with arbitrary forms of approximation, including deep learning models.

2011

• (Peter Stone, 2011a) ⇒ Peter Stone. (2011). “Q-Learning.” In: (Sammut & Webb, 2011) p.819
  • QUOTE: Q-learning is a form of temporal difference learning. As such, it is a model-free reinforcement learning method combining elements of dynamic programming with Monte Carlo estimation. Due in part to Watkins’ (1989) proof that it converges to the optimal value function, Q-learning is among the most commonly used and well-known reinforcement learning algorithms.

2001

• (Precup et al., 2001) ⇒ Doina Precup, Richard S. Sutton, and Sanjoy Dasgupta. (2001). “Off-Policy Temporal-Difference Learning with Function Approximation.” In: Proceedings of ICML-2001 (ICML-2001).
  • QUOTE: ... … , called off-policy methods. Q-learning is an off-policy method in that it learns the optimal policy even when actions are selected according to a more exploratory or even random policy. Q-learning requires only that all …

1992

• (Watkins & Dayan, 1992) ⇒ Christopher J. C. H. Watkins, and Peter Dayan. (1992). “Technical Note : [math]\displaystyle{ \cal{Q} }[/math]-Learning.” In: Machine Learning Journal, 8(3-4). doi:10.1007/BF00992698
  • ABSTRACT: [math]\displaystyle{ \cal{Q} }[/math]-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.

1989

• (Watkins, 1989) ⇒ Christopher Watkins. (1989). “Learning from Delayed Rewards.” PhD diss., King's College, Cambridge,

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