Reinforcement Learning (RL) Algorithm
A Reinforcement Learning (RL) Algorithm is an online learning algorithm that can be implemented into a reinforcement learning system to solve an online reward maximization task (to maximize a cumulative reward metric).
- AKA: Online Reward-Maximization Algorithm, RL Algorithm.
- Context:
- It can typically have an RL Reward Function to evaluate agent actions.
- It can typically maintain an RL Policy to guide agent behaviors.
- It can typically implement Value Function Approximation for state evaluation.
- It can typically balance Exploration Strategy with exploitation approaches.
- It can typically process Sequential Decision through temporal difference learning.
- It can typically update its RL behavior through iterative improvement over training episodes.
- It can typically quantify action quality through action-value functions.
- It can typically leverage discount factors to prioritize immediate rewards over future rewards.
- ...
- It can often utilize Experience Replay Buffer for sample efficiency.
- It can often employ Policy Gradient Method for direct policy optimization.
- It can often incorporate State Representation Learning for feature extraction.
- It can often implement Reward Shaping for learning guidance.
- It can often adapt to non-stationary environments through continuous learning.
- It can often employ function approximation techniques for handling large state spaces.
- It can often mitigate catastrophic forgetting through regularization methods.
- It can often leverage auxiliary tasks to improve representation learning.
- ...
- It can range from being a Model-Free Reinforcement Learning Algorithm to being a Model-Based Reinforcement Learning Algorithm, depending on its environment model usage.
- It can range from being a Single-Agent Reinforcement Learning Algorithm to being a Multi-Agent Reinforcement Learning Algorithm, depending on its agent interaction scope.
- It can range from being a Discrete Action Space Algorithm to being a Continuous Action Space Algorithm, depending on its action representation.
- It can range from being a Supervised Learning Based RL Algorithm to being a Self-Supervised RL Algorithm, depending on its learning supervision approach.
- It can range from being a Simple Tabular RL Algorithm to being a Complex Neural RL Algorithm, depending on its algorithmic complexity level.
- It can range from being an On-Policy Reinforcement Learning Algorithm to being an Off-Policy Reinforcement Learning Algorithm, depending on its policy sampling approach.
- It can range from being a Sample-Efficient Reinforcement Learning Algorithm to being a Sample-Intensive Reinforcement Learning Algorithm, depending on its data utilization efficiency.
- It can range from being a Value-Based Reinforcement Learning Algorithm to being a Policy-Based Reinforcement Learning Algorithm, depending on its optimization target.
- It can range from being a Model-Based Planning Algorithm to being a Model-Free Learning Algorithm, depending on its decision-making mechanism.
- ...
- It can be implemented by a Reinforcement Learning System for reinforcement learning tasks.
- It can integrate with Neural Network Architecture for deep reinforcement learning.
- It can utilize Markov Decision Process for state transition modeling.
- It can leverage high-performance computing for parallelized training.
- It can incorporate human domain knowledge through reward function design.
- It can employ transfer learning techniques to adapt to new environments.
- ...
- Examples:
- Classical RL Algorithms, such as:
- Dynamic Programming Based Algorithms, such as:
- Monte Carlo Methods, such as:
- Value-Based RL Algorithms, such as:
- Q-Learning Algorithms, such as:
- SARSA Algorithm for on-policy learning.
- Expected SARSA for reduced variance updates.
- Policy-Based RL Algorithms, such as:
- Actor-Critic Algorithms, such as:
- Exploration-Based Algorithms, such as:
- Automated Feedback RL Algorithms, such as:
- Human-in-the-Loop RL Algorithms, such as:
- Advanced RL Algorithms, such as:
- Specialized RL Algorithms, such as:
- Hierarchical RL Algorithms, such as:
- Meta RL Algorithms, such as:
- Multi-Task RL Algorithms, such as:
- Safe RL Algorithms, such as:
- Evolutionary RL Algorithms, such as:
- Offline RL Algorithms, such as:
- …
- Classical RL Algorithms, such as:
- Counter-Example(s):
- an Imitative Learning Algorithm, which directly mimics expert demonstrations without explicit reward optimization.
- a No-Regret Online Learning Algorithm, which optimizes for regret minimization rather than reward maximization.
- a Contextual Bandits Algorithm, which lacks sequential state transitions and focuses only on immediate reward.
- a Batch Learning Algorithm, which processes complete datasets rather than learning from online interactions.
- a Cumulative Learning Algorithm, which aggregates knowledge without necessarily using reward signals.
- an Inverse Reinforcement Learning Algorithm, which infers reward functions from observed behaviors rather than learning policys from given rewards.
- a Supervised Classification Algorithm, which requires labeled examples for training rather than learning from environmental feedback.
- an Unsupervised Clustering Algorithm, which discovers data patterns without any form of external feedback.
- a Genetic Programming Algorithm, which evolves program structures through population-based selection rather than value-based learning.
- a Planning Algorithm, which requires a complete environment model rather than learning from interaction experiences.
- See: Serial Decision Task, Average-Reward Reinforcement Learning, Efficient Exploration in Reinforcement Learning, Gaussian Process Reinforcement Learning, Inverse Reinforcement Learning, Policy Gradient Methods, Reward Shaping, Symbolic Dynamic Programming, Temporal Difference Learning, Value Function Approximation, Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, Hierarchical Reinforcement Learning, Self-Play Reinforcement Learning Algorithm, Reinforcement Learning Optimization Problem, Markov Decision Process Theory, Experience Replay Technique, Exploration-Exploitation Dilemma, Transfer Learning in RL, Meta-Reinforcement Learning Framework, Sample Efficiency in RL, Safety-Constrained Reinforcement Learning.
References
2025-05-12
- Deep Research https://chatgpt.com/c/6821742a-8b14-8012-b105-99dab075ffbb
- Question: What is a RL Algorithm and how does it function to solve online reward maximization tasks?
- Answer: A RL Algorithm is an online learning algorithm that enables an agent to make sequential decisions in an environment to maximize a cumulative reward signal. Unlike supervised learning, which relies on labeled input-output pairs, a RL agent learns through trial and error: it takes actions, observes rewards, and adjusts its behavior to improve future rewards. The problem is often formalized as a MDP, with states, actions, transition dynamics, and reward function.
- Core Components of RL Algorithms:
- Policy: The policy is the decision-making function of the agent – essentially the agent's strategy or behavior. Formally, a policy π maps states to actions (deterministically or probabilistically). It tells the agent what to do in each situation. The policy can be thought of as the "brain" of the agent that we seek to optimize. All other components in an RL algorithm serve ultimately to evaluate and improve the policy.
- Reward Function: The reward function defines the goal of the agent by mapping each state (or state-action pair) to a numerical reward signal. The reward R_t at time t is a scalar feedback from the environment indicating how desirable the current state or state transition is. A high reward means the agent is doing well at that step, while a low or negative reward indicates poor outcomes. Immediate rewards drive the agent's moment-to-moment behavior, but the agent's true objective is to maximize the long-term return, typically defined as the discounted sum of future rewards.
- Value Function and Approximation: While the reward function tells the agent what is good in an immediate sense, the value function estimates how good it is to be in a state (or to perform a certain action in a state) in the long run. The state-value function V^π(s) is defined as the expected return (cumulative future reward) starting from state s and following policy π thereafter. Similarly, the action-value function (or Q-value) Q^π(s,a) is the expected return starting from state s, taking action a, and then following π. The Value Function Approximation uses a parameterized function (like a neural network) to generalize value estimates across states, rather than storing exact values for each state.
- Exploration-Exploitation Strategies: A hallmark of RL is the exploration–exploitation dilemma: an agent must exploit its current knowledge to obtain rewards, but it also must explore new actions to discover potentially better strategies. RL algorithms therefore include strategies to balance these two needs. A simple and widely used strategy is ε-greedy exploration: the policy is followed most of the time, but with a small probability ε the agent chooses a random action, encouraging exploration of unvisited states.
- Sequential Decision Processes and Temporal-Difference Learning: RL deals with sequential decision making: the agent's actions influence not just immediate rewards but also future states and rewards. A core technique for learning in sequential environments is Temporal-Difference (TD) learning. TD methods update value estimates by bootstrapping: they adjust estimates based on other learned estimates, in accordance with the Bellman equation (which relates the value of a state to the value of subsequent states).
- Experience Replay Buffer: Modern deep RL algorithms often utilize an experience replay buffer to improve learning efficiency and stability. An experience replay buffer is essentially a memory bank that stores the agent's experiences, typically as tuples (s_t, a_t, r_t, s_{t+1}) of state, action, reward, and next state. During training, the algorithm will sample random mini-batches of past experiences from this buffer to update the value function or policy network.
- Policy Gradient Methods: In contrast to value-based methods that focus on learning value functions, policy gradient methods directly adjust the policy itself (often a parameterized stochastic policy) to maximize the expected return. These methods rely on the policy gradient theorem, which provides a formula for the gradient of the performance objective with respect to the policy parameters.
- State Representation Learning: In complex environments, especially those with high-dimensional observations (like images, video, or raw sensor readings), learning a good representation of the state is crucial. State representation learning in RL refers to techniques for transforming raw observations into more compact, informative features that make it easier for the agent to decide and predict outcomes.
- Reward Shaping: Reward shaping is a technique used to accelerate learning by providing the agent with additional feedback signals, beyond the sparse or delayed rewards of the original task. Inspired by training animals (where intermediate rewards guide behavior), reward shaping involves adding supplemental, human-designed rewards to guide the agent toward desired behaviors.
- Major Dimensions of RL Algorithms:
- Model-Free vs. Model-Based Reinforcement Learning:
- Model-Free RL: In model-free methods, the agent learns solely from direct interaction with the environment without learning an internal model of the environment's transition dynamics or reward function. The agent does not attempt to predict future states except via the learned value function; instead, it learns a policy (or value function) that maps states to actions based only on observed rewards and state transitions.
- Model-Based RL: Model-based methods involve learning or using an explicit model of the environment (the transition function and sometimes the reward function) and then using that model to help make decisions. An model can be thought of as a simulator: given a state and action, it predicts the next state (and reward). With a model in hand, an agent can plan by simulating possible future trajectories, enabling it to evaluate actions without having to try them in the real environment.
- Single-Agent vs. Multi-Agent Learning:
- Single-Agent RL: Here, there is only one learning agent in the environment. The environment's dynamics may be stochastic or complex, but other entities (if any) are typically fixed or part of the environment. The agent focuses solely on maximizing its own cumulative reward.
- Multi-Agent RL: In multi-agent settings, there are multiple agents, each with their own policy and possibly their own reward function, coexisting in the environment. The agents may be cooperative (working toward a common goal), competitive (with opposing goals, as in a zero-sum game), or a mix of both.
- Discrete vs. Continuous Action Spaces:
- Discrete Action Spaces: The agent has a fixed, countable set of actions to choose from at each decision point. Examples include moves like {up, down, left, right} in a grid world, or pressing buttons on a controller in an Atari game. Many classical RL techniques were developed for discrete spaces.
- Continuous Action Spaces: Here, actions are drawn from continuous domains – for instance, a robot arm can move in a continuum of angles or a self-driving car can apply a continuous steering angle and acceleration. In continuous spaces, the agent might choose a real-valued vector (e.g., torque values for motors).
- Supervised Learning Signals vs. Self-Supervised/Reward-Only Learning:
- Supervised Learning in RL (Imitation & Hybrid Approaches): In some cases, an algorithm might be provided with example trajectories or expert decisions, using them as supervised data to guide learning. One common approach is Imitation Learning (or behavioral cloning), where the agent learns a policy by mimicking an expert's state-action pairs.
- Pure RL (Self-Supervised Learning by Reward): In pure reinforcement learning, the agent receives feedback only in the form of rewards from the environment. It must effectively teach itself by exploring and observing which actions yield higher returns.
- Tabular vs. Function Approximation (Complexity of Representations):
- Tabular Methods: These methods explicitly maintain values or policies for each state (or state-action pair) in a table. They are feasible only when the state space (and action space) is small and discrete (enumerable).
- Function Approximation / Neural Network-Based Methods: Here, instead of a table, the algorithm uses a parameterized function (linear models, neural networks, decision trees, etc.) to represent the policy or value function. This allows generalization – the function can predict values for states it hasn't seen by extrapolating from similar states.
- On-Policy vs. Off-Policy Learning:
- On-Policy Algorithms: An on-policy method learns the value of the policy currently being executed (including whatever exploratory actions it takes). In other words, the data used for learning is collected while following the same policy that the algorithm is trying to improve.
- Off-Policy Algorithms: Off-policy methods learn the value of one policy while potentially following another. Concretely, an off-policy algorithm can learn from data generated by a behavior policy that is different from the target policy it's trying to optimize.
- Sample-Efficient vs. Sample-Intensive Algorithms:
- Sample-Efficient Approaches: These algorithms make the most out of each interaction. They might utilize techniques like model-based simulation, off-policy data reuse, transfer learning, or rich inductive biases to reduce the number of real environment steps needed.
- Sample-Intensive Approaches: On the other end, some methods need an extremely large number of trials to learn effectively. Early deep RL algorithms fell in this category: e.g., DQN required on the order of 10^7 – 10^8 frames to learn Atari games at human level, which corresponds to many days of game time.
- Model-Free vs. Model-Based Reinforcement Learning:
- Core Components of RL Algorithms:
- Citations:
[1] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press. [2] Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. [3] Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. [4] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
2024
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Reinforcement_learning Retrieved:2024-4-10.
- Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the long term reward, whose feedback might be incomplete or delayed.
The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the Markov decision process and they target large Markov decision processes where exact methods become infeasible.[1]
- Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
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2019a
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Reinforcement_learning Retrieved:2019-5-12.
- Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is considered as one of three machine learning paradigms, alongside supervised learning and unsupervised learning.
It differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). ...
- Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is considered as one of three machine learning paradigms, alongside supervised learning and unsupervised learning.
2019b
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Q-learning Retrieved:2019-5-12.
- Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells 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 a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.[1] 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 returns the reward used to provide the reinforcement and can be said to stand for the "quality" of an action taken in a given state.[2]
- Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells 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.
- ↑ Melo, Francisco S. "Convergence of Q-learning: a simple proof" (PDF).
- ↑ Matiisen, Tambet (December 19, 2015). "Demystifying Deep Reinforcement Learning". neuro.cs.ut.ee. Computational Neuroscience Lab. Retrieved 2018-04-06.
2017
- (Stone, 2017) ⇒ Peter Stone (2017). Reinforcement Learning. In: Sammut & Webb (2017).
- QUOTE: Reinforcement learning describes a large class of learning problems characteristic of autonomous agents interacting in an environment: sequential decision-making problems with delayed reward. Reinforcement-learning algorithms seek to learn a policy (mapping from states to actions) that maximizes the reward received over time.
Unlike in supervised learning problems, in reinforcement-learning problems, there are no labeled examples of correct and incorrect behavior. However, unlike unsupervised learning problems, a reward signal can be perceived.
Many different algorithms for solving reinforcement-learning problems are covered in other entries. This entry provides just a brief high-level classification of the algorithms. Perhaps the most well-known approach to solving reinforcement-learning problems, as covered in detail by Sutton and Barto (1998), is based on learning a value function, which represents the long-term expected reward of each state the agent may encounter, given a particular policy.
- QUOTE: Reinforcement learning describes a large class of learning problems characteristic of autonomous agents interacting in an environment: sequential decision-making problems with delayed reward. Reinforcement-learning algorithms seek to learn a policy (mapping from states to actions) that maximizes the reward received over time.
2016
- (Krakovsky, 2016) ⇒ Marina Krakovsky. (2016). “Reinforcement Renaissance.” In: Communications of the ACM Journal, 59(8). doi:10.1145/2949662
- QUOTE: The two types of learning — reinforcement learning and deep learning through deep neural networks — complement each other beautifully, says Sutton. " Deep learning is the greatest thing since sliced bread, but it quickly becomes limited by the data, " he explains. " If we can use reinforcement learning to automatically generate data, even if the data is more weakly labeled than having humans go in and label everything, there can be much more of it because we can generate it automatically, so these two together really fit well. “ Despite the buzz around DeepMind, combining reinforcement learning with neural networks is not new. TD-Gammon, a backgammon-playing program developed by IBM's Gerald Tesauro in 1992, was a neural network that learned to play backgammon through reinforcement learning (the TD in the name stands for Temporal-Difference learning, still a dominant algorithm in reinforcement learning). “Back then, computers were 10,000 times slower per dollar, which meant you couldn't have very deep networks because those are harder to train ... “Deep reinforcement learning is just a but word for traditional reinforcement learning combined with deeper neural networks, " he says.
1998
- (Sutton & Barto, 1998) ⇒ Richard S. Sutton, and Andrew G. Barto. (1998). “Reinforcement Learning: An introduction." MIT Press. ISBN:0262193981
- BOOK OVERVIEW: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment....
1996
- (Kaelbling et al., 1996) ⇒ L. P. Kaelbling, M. L. Littman, and A. W. Moore. (1996). “Reinforcement Learning: A Survey.” In: Journal of Artificial Intelligence Research, Vol 4, (1996), 237-285
- ABSTRACT: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word “reinforcement. The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
1997
- (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill.
1990
- (Sutton, 1990) ⇒ Richard S. Sutton. (1990). “Integrated Architecture for Learning, Planning, and Reacting based on Approximating Dynamic Programming.” In: Proceedings of the seventh international conference (1990) on Machine learning. ISBN:1-55860-141-4
- QUOTE: This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world.