Cs 234 assignment Study guides, Class notes & Summaries

CS 234 Winter 2021 Assignment 1 Due: January 22 at 6:00 pm (PST) For submission instructions please refer to website For all problems, if you use an existing result from either the literature or a textbook to solve the exercise, you need to cite the source. 1 Flappy Karel MDP [25 pts] There is a hot new mobile game on the market called Flappy Karel, where Karel the robot must dodge the red pillars of doom and flap its way to the green pasture. Consider the following 2 grid environments (...

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CS 234 ASSIGNMENT 2 2021/2022 – Stanford University. Distributions induced by a policy (13 pts) In this problem, we’ll work with an infinite-horizon MDP M = hS, A, R, T , γi and consider stochastic policies of the form π : S → ∆(A) 1 . Additionally, we’ll assume that M has a single, fixed starting state s 0 ∈ S for simplicity. (a) (written, 3 pts) Consider a fixed stochastic policy and imagine running several rollouts of this policy within the environment. Naturally, depe...

CS 234 ASSIGNMENT 2 2021/2022.0 Distributions induced by a policy (13 pts) In this problem, we’ll work with an infinite-horizon MDP M = hS, A, R, T , γi and consider stochastic policies of the form π : S → ∆(A) 1 . Additionally, we’ll assume that M has a single, fixed starting state s 0 ∈ S for simplicity. (a) (written, 3 pts) Consider a fixed stochastic policy and imagine running several rollouts of this policy within the environment. Naturally, depending on the stochastici...

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CS 234 Winter 2022: Assignment #2 Introduction In this assignment we will implement deep Q-learning, following DeepMind’s paper ([1] and [2]) that learns to play Atari games from raw pixels. The purpose is to demonstrate the effectiveness of deep neural networks as well as some of the techniques used in practice to stabilize training and achieve better performance. In the process, you’ll become familiar with PyTorch. We will train our networks on the Pong-v0 environment from OpenAI g...

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Consider the following grid environment. Starting from any unshaded square, you can move up, down, left, or right. Actions are deterministic and always succeed (e.g. going left from state 16 goes to state 15) unless they will cause the agent to run into a wall. The thicker edges indicate walls, and attempting to move in the direction of a wall results in staying in the same square (e.g. going in any direction other than left from state 16 stays in 16). Taking any action from the green target...

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