8b: Policy Learning and Deep RL Policy Learning Policy learning algorithms do not use a value function but instead operate directly on the policy, chosen from a family of policies determined by parameters .π :θ S ↦ A θ Typically, is a neural network with weights which takes a state as input and produces action […]
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8a: Reinforcement Learning Week 8: Overview The topic for this week is Reinforcement Learning (RL). We will see how reinforcement learning tasks can be formally de�ned, compare di�erent models of optimality, and discuss the need for exploration and the problem of delayed rewards. We will present a number of RL algorithms including Temporal Di�erence learning
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CSE 101 Exam 2 Review CSE 101 Exam 2 Review Shortest Paths (Ch 4) BFS Dijkstra Priority Queues Bellman-Ford Shortest Paths in DAGs Fundamental Shortest Paths Formula dist(v) ℓ(v,w) s w v Observation If there is a length ≤d s-v path, then there is some w adjacent to v with a length ≤(d-1) s-w path.
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CM30173: Cryptography\reserved@d =[@let@token art IV CM30173: Cryptography Part IV The ElGamal signature scheme Security of the ElGamal signature scheme Careless uses of ElGamal signature scheme A downside of ElGamal ElGamal variants A problem… Part VI Signature schemes CM30173: Cryptography Part IV The ElGamal signature scheme Security of the ElGamal signature scheme Careless uses of ElGamal
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Announcements Announcements Homework 4 due on Friday Slight correction to original problem 3. If you downloaded before Monday, you should check out the latest version. Exam 2 solutions on course webpage Last Time Greedy Algorithms Minimum Spanning Tree Given a weighted graph G, find a collection of edges that connect G and have no cycles
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Announcements Announcements • Homework 0 Solutions online • Homework 1 Due on Friday • Exam 1 Next Friday – If you cannot take the exam during class hours, please email me at letting me know what times might work for you. mailto: Strongly Connected Components Definition: In a directed graph G, two vertices v and
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Microsoft PowerPoint – CSE101 1 Algorithms.pptx O. Braun 1Algorithms and Optimization Problems Algorithms O. Braun 2Algorithms and Optimization Problems Algorithmic Framework An algorithm is a method for solving a problem (on a computer). 1. What problem are we solving? problem specification 2. How do we solve the problem? algorithm description 3. Why do these steps solve the problem? proof of correctness 4. When do we get an answer? time analysis Problem solving = “The Spirit of Computing”, driven by real‐world necessity: • Logistics • Scheduling: production planning, resource allocation, … • Bin Packing: storage on container ships, airline logistics, … • Shortest paths: warehouses, factory hall, distribution center, roads, … • DNA Sequencing • Evolutionary Trees (edit distance, Steiner trees…) • Finding homologues, evolutionary significance (string‐matching) • Conformational Analysis •
Announcements Announcements • Exam 3 on Friday NP-Completeness (Ch 8) • NP-Problems • Reductions • NP-Completeness & NP-Hardness • SAT • Hamiltonian Cycle • Zero-One Equations • Knapsack Brute Force Algorithms For almost every problem we have seen there has been a (usually bad) naïve algorithm that just considers every possible answer and returns the
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CSE 101 Midterm Exam Page 1 of 8 1. Assignment [15 points] Consider the following table: T1 T2 T3 T4 A 8 7 2 9 B 7 3 4 6 C 8 1 8 5 D 4 9 6 7 1.a. [4 points] Compute a feasible solution with the Smallest-Number-Heuristic. Give the assignment and the
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Question 1 (Knapsack, 30 points). Consider the knapsack problem where you are allowed to take items more than once, have a total capacity of 10 and have the following items available: Item Weight Value A 2 4 B 3 5 C 4 9 D 5 10 E 7 16 What is the best combination of