Policy
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A policy in reinforcement learning is a way of behaving. Specifically, a policy is a mapping from states to probabilities of selecting each possible action \(\pi(a|s)\).
Links to this note:
- Reinforcement Learning
- Actor Critic
- Off-policy Reinforcement Learning
- henderson2018deep: Deep Reinforcement Learning That Matters
- Bellman Equation
- Interview Review Material
- badia2020agent57: Agent57: Outperforming the Atari Human Benchmark
- barreto2018successor: Successor Features for Transfer in Reinforcement Learning
- General Value Functions
- jaderberg2017reinforcement: Reinforcement Learning with Unsupervised Auxiliary Tasks
- kostas2019asynchronous: Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock
- lehnert2017advantages: Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
- mnih2014recurrent: Recurrent Models of Visual Attention
- Model-based RL
- Reinforcement Learning: An Introduction
- scholkopf2019causality: Causality for Machine Learning
- sutton2011horde: Horde: A Scalable Real-time Architecture for Learning Knowledge from Unsupervised Sensorimotor Interaction
- veeriah2019discovery: Discovery of Useful Questions as Auxiliary Tasks
- wang2017learning: Learning to reinforcement learn
- white2015developing: Developing a predictive approach to knowledge
- white2017unifying: Unifying Task Specification in Reinforcement Learning
- mohamed2019monte: Monte Carlo Gradient Estimation in Machine Learning
- liu2018breaking: Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
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