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Introduction to Reinforcement Learning
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Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI), focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. This paper provides an overview of RL, covering its core concepts, methodologies, and resources for further learning. It offers a thorough explanation of fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. Additionally, the paper presents a variety of RL algorithms, categorized based on the key factors such as model-free, model-based, value-based, policy-based, and other key factors. Resources for learning and implementing RL, such as books, courses, and online communities are also provided. By offering a clear, structured introduction, this paper aims to simplify the complexities of RL for beginners, providing a straightforward pathway to understanding.
Forward citations
Cited by 4 Pith papers
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Homing through Reinforcement Learning
In a 2D Q-learning homing model, mean homing time is reported to be non-monotonic in rotational diffusion with a crossover at D_r≈12, and the learned policy is claimed to beat a stochastic-resetting ABP baseline.
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Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization
A TD3 agent with Dirichlet priors and quantum-inspired genetic mutation matches Apple's historical R&D/SG&A splits on held-out quarters, but the evaluation metric nearly reproduces the training objective.
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One STEP at a time: Language Agents are Stepwise Planners
A stepwise planner with memory-guided execution and evaluation raises ScienceWorld scores to 67.4, outperforming CLIN and published SOTA baselines.
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A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
A comprehensive but flawed survey of RL algorithms that catalogs many methods and applications without rigorous comparative analysis.
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