Reinforcement Learning Algorithms enable agents to learn optimal decision-making policies through interaction with environments, guided by reward signals. Core families include value-based methods (Q-learning, DQN), policy gradient methods (REINFORCE, PPO, TRPO), and actor-critic approaches (A3C, SAC), all of which balance exploration against exploitation to maximise cumulative expected reward.
Semantic Classification
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Key Characteristics
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Learns through trial-and-error interaction with environments
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Balances exploration and exploitation strategies
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Handles sequential decision-making with delayed rewards
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Scales to high-dimensional state and action spaces
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Incorporates model-free and model-based approaches
Overview
Reinforcement Learning Algorithms enable agents to learn optimal decision-making policies through interaction with environments, guided by reward signals. Core algorithms include value-based methods (Q-learning, DQN), policy gradient methods (REINFORCE, PPO, TRPO), actor-critic approaches (A3C, SAC), and model-based RL. Advanced techniques incorporate deep neural networks for function approximation, experience replay, target networks, and exploration strategies. Applications span robotics, game playing, autonomous systems, resource management, and personalized recommendations.
Related Concepts
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References
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Sutton, R. & Barto, A. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
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Mnih, V. et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
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Schulman, J. et al. (2017). Proximal Policy Optimization Algorithms. arXiv:1707.06347.