Lu, Yukuan

Sources:

1. --> Documentation
2. --> Turorial

ViZDoom allows developing AI bots that play Doom using only visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.

Sources:

1. UWashington: CSE378, Lecture09
2. UWashington: CSE378, Lecture10
3. John L. Hennessy & David A. Patterson. (2019). Appendix C.1. Computer Architecture: A Quantitative Approach (6th ed.). Elsevier Inc.
4. Randal E. Bryant & David R. O’Hallaron. (2016). Chapter 4. Processor Architecture. Computer Systems: A Programmer’s Perspective (3th ed., pp. 387-516). Pearson.

Note: the assembly code in this article can be MIPS or RISCV. This shouldn't be consufusing since the only big difference between them is that MIPS add a $ before the name of each register:

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# MIPS:
add $t0, $t1, $t2  # add values in $t1 and $t2, the result is stored in $t0

# RISC-V
add t0, t1, t2 # add values in t1 and t2, the result is stored in t0

Sources:

1. John L. Hennessy & David A. Patterson. (2019). Chapter 2. Memory Hierarchy Design. Computer Architecture: A Quantitative Approach (6th ed., pp. 78-148). Elsevier Inc.
2. RISC-V instruction format
3. ECS201A: ISAs and Machine Representation

Sources:

1. UWashington: CSE471, Lecture04
2. UCSD: CSE240A, Lecture13
3. John L. Hennessy & David A. Patterson. (2019). Chapter 3. Instruction-Level Parallelism and Its Exploitation. Computer Architecture: A Quantitative Approach (6th ed., pp. 168-266). Elsevier Inc.

Sources:

1. UWashington: CSE378, Lecture13
2. John L. Hennessy & David A. Patterson. (2019). Chapter 1. Fundamentals of Quantitative Design and Analysis. Computer Architecture: A Quantitative Approach (6th ed.). Elsevier Inc.

Sources:

1. CMU CS651: P, NP, and NP-Completeness

Sources:

1. SSH Essentials: Working with SSH Servers, Clients, and Keys

Useful self-hosted docker images which can be hosted on a remote server and be visited on the local browser.

1. Shiyu Zhao. Chapter 5: Monte Carlo Methods. Mathematical Foundations of Reinforcement Learning.
2. --> Youtube: MC Basic

Sources:

1. Shiyu Zhao. Chapter 5: Monte Carlo Methods. Mathematical Foundations of Reinforcement Learning
2. Shiyu Zhao. Chapter 6: Stochastic Approximation. Mathematical Foundations of Reinforcement Learning

Mean estimation: compute \(\mathbb{E}[X]\) using \(\left\{x_k\right\}\) \[ w_{k+1}=w_k-\frac{1}{k}\left(w_k-x_k\right) \]