By C. Chandler King
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Additional info for NAvion: The North American Aviation, Inc
The instance-based approximator must now estimate s4 from these instances, which is equivalent to estimating T (s4 , a). s3 s2 s4 s1 s’1 22 2 Reinforcement Learning Background such methods generally need to determine which instances are necessary to store so that the memory requirements are not unbounded. 3 we will discuss one such model-learning method that uses instance-based approximation to learn in continuous state spaces. 3 Learning Methods This section discusses different approaches to learning policies in MDPs.
Rather than doing a single update at each timestep, recently visited (s, a) pairs also share some of the update because they are partially “responsible” for the agent’s current situation. New (s, a) pairs are set to have an eligibility of 1 and on each update all eligibilities are decayed by a fixed parameter, typically denoted λ . 3 Learning Methods 25 Algorithm 3. 2 1 if s = st and a=at λ et−1 (s, a) otherwise NeuroEvolution of Augmenting Topologies (NEAT) NeuroEvolution of Augmenting Topologies [Stanley and Miikkulainen (2002)] (NEAT) is used in this monograph as a representative policy search method for RL.
Batch methods: On-line methods require that the agent update its knowledge as it interacts with the environment. Batch, or offline, methods are designed to be more sample efficient, as they can store environmental interaction data and use the set multiple times to learn to approximate Q or π . Additionally, such methods allow a clear separation of the learning mechanism from the exploration mechanism (which much decide whether to attempt to gather more data about the environment or exploit the current best policy).
NAvion: The North American Aviation, Inc by C. Chandler King