# CSE510 Deep Reinforcement Learning (Lecture 1)

## Artificial general intelligence

- Multimodeal perception
- Persistent memory + retrieval
- World modeling + planning
- Tool use with verification
- Interactive learning loops (RLHF/RLAIF)
- Uncertainty estimation & oversight

LLM may not be the ultimate solution for AGI, but may be a part of solution.

## Long-Horizon Agency

Decision-Making/Control and Multi-Agent collaboration

## Course logistics

Announcement and discussion on Canvas

Weekly recitations

Thursday 4:00PM- 5:00PM in Mckelvey Hall 1030

or night office hours (11am-12pm Wed in Mckelvey Hall 2010D)

or by appointment

### Prerequisites

- Proficiency in Python programming.
- **Programming experience with deep learning**.
- Research Experience (Not required, but highly recommended)
- Mathematics: Linear Algebra (MA 429 or MA 439 or ESE 318), Calculus III (MA 233), Probability & Statistics.

### Textbook

Not required, but recommended:

- Sutton & Barto, Reinforcement Learning: An Introduction (2nd ed., online).
- Russell & Norvig, Artificial Intelligence: A Modern Approach (4th ed.).
- OpenAI Spinning Up in Deep RL tutorial.

### Final Project

Research-level project of your choice

- Improving an existing approach
- Tackling an unsolved task/benchmark
- Creating a new task/problem that hasn't been addressed by RL

Can be done in a team of 1-2 students

Must be harder than homework.

The core is to understand the pipeline of RL research, may not always be an improvement over existing methods.

#### Milestones

- Proposal (max 2 pages)
- Progress report with brief survey (max 4 pages)
- Presentation/Poster session
- Final report (7-10 pages, NeurIPS style)

## What is RL?

### Goal for course

How to build intelligent agents that **learn to act** and achieve specific goals in a **dynamic environments**?

Acting to achieve is key part of intelligence.

> Brain is to produce adaptable and complex movements. (Daniel Wolpert)

## What RL do

A general-purpose framwork for decision making/behavioral learning

- RL is for an agent with the capacity to act
- Each action influences the agent's future observation
- Success is measured by a scalar reward signal
- Goal: find a policy that maximize expected total rewards.

Exploration: Add randomness to your action selection

If the result was better than expected, do more of the same in the future.

### Deep reinforcement learning

DL is a general-purpose framework for representation learning.

- Given an objective
- Learn representation that is required to achieve objective
- Directly from raw inputs
- Using minimal domain knowledge

Deep learning enables RL algorithms to solve complex problems in an end-to-end manner.

### Machine learning Paradigm

Supervised learning: learning from examples

Self-supervised learning: learning structures in data

Reinforcement learning: learning from experiences

Example using LLMs:

Self-supervised: pretraining

SFT: supervised fine-tuning (post-training)

RL is also used in post-training for improving reasoning capabilities.

RLHF: reinforcement learning from human feedback (fine-tuning)

_RL generates data beyond the original training data._

All the paradigm are "supervised" by a loss function.

### Differences for RL from other paradigms

**Exploration**: the agent does not have prior data known to be good.

**Non-stationarity**: the environment is dynamic and the agent's actions influence the environment.

**Credit assignment**: the agent needs to learn to assign credit to its actions. (delayed reward)

**Limited samples**: actions take time to execute in the real world, which may limited the amount of experience.

