Previously attributed to insufficiently flexible function approximation and noise. However, overestimations can occur regardless of the source of approximation error. Since imprecise value estimates are often used for learning, overestimations may be much more common.

The Problem With Q-learning

DQN is a method using neural network to generate Q-value and select actions instead of forms in original Q-learning method.

Deep Q Networks (DQN)

Double Q learning introduces double estimator to deal with the over-estimation problem. For update of Q values, both estimators use the value of the other estimator. For selecting options, it's still a greedy algorithm based on the average value of two estimators.

Double Q-learning

One of the most popular reinforcement learning algorithms, known to sometimes learn unrealistically high action values since it includes a maximization step that tends to prefer overestimated values.

University of Michigan - Ann Arbor

University of Toledo

The fundamental concepts that form the basis of reinforcement learning are:

 - Agent
 - Environment
 - Action
 - State
 - Reward

The agent learns through repeated interaction with the environment. To be successful, the agent needs to:
 - Learn the interaction between states, actions, and subsequent rewards.
 - Determine which action will provide the optimal outcome.

Fundamental Concepts for Reinforcement Learning

van Hasselt, H., Guez, A., & Silver, D. (2016). Deep Reinforcement Learning with Double Q-Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). Retrieved from https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q

Deep Reinforcement Learning with Double Q-learning

Here is a useful website for starters to understand reinforcement learning: https://missinglink.ai/guides/deep-learning-healthcare/tensorflow-reinforcement-learning-introduction-and-hands-on-tutorial/

Useful Website for Reinforcement learning

In reinforcement learning, the environment encompasses everything external to the agent with which it interacts. It processes the agent's action in its current state and responds by providing a reward and transitioning the agent to a new state. For example, in a physical system, the environment could be the laws of physics. From the agent's perspective, the environment often functions as a black box.

Environment in Reinforcement Learning

In reinforcement learning, a state (s) represents the current situation or configuration of the environment. It is a snapshot of all relevant information at a specific moment that the agent uses to make a decision. This information can include the agent's position, the status of other entities, and any other data that defines the current circumstances.

State in Reinforcement Learning

In reinforcement learning, the agent is the component that functions as the learner or decision-maker. It interacts with an environment by perceiving its state, performing actions, and learning from the resulting feedback. For instance, an agent could be a robot navigating a path or a trading algorithm making financial decisions. In the context of Large Language Models (LLMs), the LLM itself often serves as the agent.

Agent in Reinforcement Learning

An action is the set of all possible moves the agent can make. Agents choose from a list of possible actions. In video games, for example, the list might include: running right or left, jumping high or low, crouching or standing still.

Action in Reinforcement Learning

In reinforcement learning, a reward is a signal sent from the environment to the agent that provides feedback on an action's success. This feedback, which can be positive or negative, guides the agent's learning process by indicating the desirability of the actions taken. The agent's primary objective is to modify its policy over time to maximize the cumulative reward it receives.

Reward in Reinforcement Learning

Here is a free pdf version of a book about Reinforcement Learning:
http://incompleteideas.net/book/RLbook2020.pdf

Useful Book for Reinforcement Learning

https://cims.nyu.edu/~donev/Teaching/WrittenOral/Projects/XintianHan-WrittenAndOral.pdf
https://www.davidsilver.uk/teaching/     
(David Silver's tutorial of reinforcement learning)
http://rail.eecs.berkeley.edu/deeprlcourse/
https://katefvision.github.io/
http://incompleteideas.net/rlai.cs.ualberta.ca/RLAI/RLAIcourse/RLAIcourse2006.html
http://web.stanford.edu/class/cs234/schedule.html

Useful Tutorials about Math behind Reinforcement Learning

Reinforcement learning is a general approach to solve reward-based problem. The building of it is based on mathematical foundations. We can also view it as a sequential decision making and control problem mathematically. For example, when driving a car, we need to make decisions about turning left/right or going straight after the previous decisions. And the key ingredient in reinforcement learning is Markov Decision Process.

Math Behind Reinforcement Learning

It is very challenging in the reinforcement learning to find a perfect balance between using previously used effective action(**exploitation**) and exploring new ones (**exploration**).

In order to solve this problem, simple $\epsilon$-Greedy algorithm can be used, in which we with some small probability $\epsilon$ we would select action randomly (**exploration**) and with $1 - \epsilon$ probability use previously selected effective action(**exploitation**).  

Exploration/Exploitation trade-off

Classification of Reinforcement Learning Methods

On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data. Classicla on-policy methods include Sarsa Algorithm. As a comparison, Q-learning method is a classical off-policy method.

On-policy vs Off-policy

Compared to value based methods, actor-critic methods focus on modeling the probability distribution of policies. For value-based methods, the logic is more like a greedy algorith, we want to maximize the value function for every step. As a result, we need to search through the action space to get the best action. But in actor-critic method, they are like using actor to perform actions based on the probability distribution of policies and using critics to judge the performance and adjust the probability distribution. They don't directly always choose the best action. 

Actor-Critic Methods

Q-learning

Borges, Alexandre and Arlindo L. Oliveira. “Combining Off and On-Policy Training in Model-Based Reinforcement Learning.” ArXiv abs/2102.12194 (2021): n. pag.
https://arxiv.org/pdf/2102.12194.pdf

Combining Off and On-Policy Training in Model-Based
Reinforcement Learning

MuZero is a model-based reinforcement learning method which contains a representation function, a dynamics function, and a prediction function.The representation function encodes an observation sequence into a hidden state. The dynamics function evaluates the current hidden state and a potential action to determine the potential next hidden state and a reward value. Lastly, the prediction function is the policy generation function. They use similar parameters and are optimized according to a combined loss function.

A Monte Carlo Tree Search (MCTS) is often used when training to evaluate action trees from the current/ root state.

MuZero

In a reinforcement learning framework, the process of training a Large Language Model (LLM) iteratively evaluates and improves the model's policy. At each step $$t$$, the current state $$s_t$$ is defined by the initial input prompt $$\mathbf{x}$$ and the tokens generated so far $$\mathbf{y}_{<t}$$. The LLM acts as the policy, denoted by the predicted distribution $$\Pr(y_t|\mathbf{x},\mathbf{y}_{<t})$$, to choose an action $$a_t$$, which is the next token $$y_t$$. After $$y_t$$ is predicted, a reward model evaluates the sequence $$(\mathbf{x},\mathbf{y}_{<t}, y_t)$$ to determine how well it aligns with the desired textual outcome. This evaluation produces reward scores that are then used to compute the value functions $$V(s_t)$$ and $$Q(s_t,a_t)$$. Finally, these value functions provide the necessary feedback to guide the subsequent training and refinement of the LLM's policy.

Reinforcement Learning Process for LLMs

A robot is being trained to find the exit in a maze. At any given position, the robot can choose to move forward, turn left, or turn right. The system keeps track of the robot's current coordinates within the maze. The robot's objective is to reach the exit as efficiently as possible. It receives a large positive signal for reaching the exit, a large negative signal for hitting a wall, and a small negative signal for each step it takes. Based on this scenario, identify the five fundamental components of this learning system and briefly explain the specific role of each.

Analyzing a Learning System

A robot is being trained to navigate a maze to find a piece of cheese. Analyze this scenario by matching each element of the training process to its corresponding fundamental concept.

The core of reinforcement learning is the interaction between an agent and a dynamic environment, which is modeled as a sequential process. At every time step, the agent assesses the environment's current state and uses its policy to select an action. After executing the action, the environment provides feedback consisting of a reward and a new state. This cycle of observing, acting, and receiving feedback continues until the agent accomplishes its objective.

Agent-Environment Interaction Loop in Reinforcement Learning

A cat is learning to use a new automated feeder that dispenses food when a lever is pressed. Initially, the cat paws at the lever randomly. After several attempts, it presses the lever and food is dispensed. The cat begins to press the lever more frequently. Which of the following statements best analyzes the relationship between the core components in this learning scenario?

Learn Before

Related

Learn After