- Reinforcement Learning - Microsoft Research
- What distinguishes reinforcement learning from deep learning and machine learning?
- CS234: Reinforcement Learning Winter 12222
- A Beginner's Guide to Deep Reinforcement Learning
Hopefully, this review is helpful enough so that newbies would not get lost in specialized terms and jargons while starting. A couple of exciting news in Artificial Intelligence AI has just happened in recent years. AlphaGo defeated the best professional human player in the game of Go. Very soon the extended algorithm AlphaGo Zero beat AlphaGo by without supervised learning on human knowledge.
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After knowing these, it is pretty hard not to be curious about the magic behind these algorithms — Reinforcement Learning RL. We will first introduce several fundamental concepts and then dive into classic approaches to solving RL problems. Hopefully, this post could be a good starting point for newbies, bridging the future study on the cutting-edge research.
Say, we have an agent in an unknown environment and this agent can obtain some rewards by interacting with the environment. The agent ought to take actions so as to maximize cumulative rewards. In reality, the scenario could be a bot playing a game to achieve high scores, or a robot trying to complete physical tasks with physical items; and not just limited to these. An agent interacts with the environment, trying to take smart actions to maximize cumulative rewards.
The goal of Reinforcement Learning RL is to learn a good strategy for the agent from experimental trials and relative simple feedback received. With the optimal strategy, the agent is capable to actively adapt to the environment to maximize future rewards. The agent is acting in an environment. How the environment reacts to certain actions is defined by a model which we may or may not know. The agent can stay in one of many states of the environment, and choose to take one of many actions to switch from one state to another.
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Which state the agent will arrive in is decided by transition probabilities between states. Once an action is taken, the environment delivers a reward as feedback. The model defines the reward function and transition probabilities. We may or may not know how the model works and this differentiate two circumstances:. Each state is associated with a value function predicting the expected amount of future rewards we are able to receive in this state by acting the corresponding policy. In other words, the value function quantifies how good a state is.
Both policy and value functions are what we try to learn in reinforcement learning.
Summary of approaches in RL based on whether we want to model the value, policy, or the environment. The interaction between the agent and the environment involves a sequence of actions and observed rewards in time,. During the process, the agent accumulates the knowledge about the environment, learns the optimal policy, and makes decisions on which action to take next so as to efficiently learn the best policy.
Reinforcement Learning - Microsoft Research
The model is a descriptor of the environment. With the model, we can learn or infer how the environment would interact with and provide feedback to the agent. The model has two major parts, transition probability function and reward function. It is a mapping from state s to action a and can be either deterministic or stochastic:. Value function measures the goodness of a state or how rewarding a state or an action is by a prediction of future reward.
The future reward, also known as return , is a total sum of discounted rewards going forward. The state-value of a state s is the expected return if we are in this state at time t, :. Additionally, since we follow the target policy , we can make use of the probility distribution over possible actions and the Q-values to recover the state-value:.
Or in other words, the future and the past are conditionally independent given the present, as the current state encapsulates all the statistics we need to decide the future.
What distinguishes reinforcement learning from deep learning and machine learning?
The agent-environment interaction in a Markov decision process. Image source: Sec. A Markov deicison process consists of five elements , where the symbols carry the same meanings as key concepts in the previous section, well aligned with RL problem settings:. A fun example of Markov decision process: a typical work day.
Image source: randomant. Bellman equations refer to a set of equations that decompose the value function into the immediate reward plus the discounted future values. The recursive update process can be further decomposed to be equations built on both state-value and action-value functions.
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As we go further in future action steps, we extend V and Q alternatively by following the policy. Illustration of how Bellman expection equations update state-value and action-value functions. If we are only interested in the optimal values, rather than computing the expectation following a policy, we could jump right into the maximum returns during the alternative updates without using a policy.
RECAP: the optimal values and are the best returns we can obtain, defined here. If we have complete information of the environment, this turns into a planning problem, solvable by DP. Unfortunately, in most scenarios, we do not know or , so we cannot solve MDPs by directly applying Bellmen equations, but it lays the theoretical foundation for many RL algorithms.
Now it is the time to go through the major approaches and classic algorithms for solving RL problems. In future posts, I plan to dive into each approach further. When the model is fully known, following Bellman equations, we can use Dynamic Programming DP to iteratively evaluate value functions and improve policy. The Generalized Policy Iteration GPI algorithm refers to an iterative procedure to improve the policy when combining policy evaluation and improvement. In GPI, the value function is approximated repeatedly to be closer to the true value of the current policy and in the meantime, the policy is improved repeatedly to approach optimality.
This policy iteration process works and always converges to the optimality, but why this is the case? Say, we have a policy and then generate an improved version by greedily taking actions,. The value of this improved is guaranteed to be better because:. Monte-Carlo MC methods uses a simple idea: It learns from episodes of raw experience without modeling the environmental dynamics and computes the observed mean return as an approximation of the expected return.
To compute the empirical return , MC methods need to learn from complete episodes to compute and all the episodes must eventually terminate. This way of approximation can be easily extended to action-value functions by counting s, a pair. TD learning methods update targets with regard to existing estimates rather than exclusively relying on actual rewards and complete returns as in MC methods.
CS234: Reinforcement Learning Winter 12222
This approach is known as bootstrapping. Be prepared, you are gonna see many famous names of classic algorithms in this section. The idea follows the same route of GPI :. In step 3. Image source: Replotted based on Figure 6. Theoretically, we can memorize for all state-action pairs in Q-learning, like in a gigantic table.
You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD temporal difference learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods.
Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
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A Beginner's Guide to Deep Reinforcement Learning
Jonathan Sanito Senior Content Developer. Adith Swaminathan Researcher. Kenneth Tran Principal Research Engineer. Katja Hofmann Researcher. Matthew Hausknecht Researcher. Reinforcement learning is an approach to machine learning that is inspired by behaviorist psychology. It is similar to how a child learns to perform a new task. Reinforcement learning contrasts with other machine learning approaches in that the algorithm is not explicitly told how to perform a task, but works through the problem on its own.
As an agent, which could be a self-driving car or a program playing chess, interacts with its environment, receives a reward state depending on how it performs, such as driving to destination safely or winning a game. Conversely, the agent receives a penalty for performing incorrectly, such as going off the road or being checkmated.
The agent over time makes decisions to maximize its reward and minimize its penalty using dynamic programming. The advantage of this approach to artificial intelligence is that it allows an AI program to learn without a programmer spelling out how an agent should perform the task. Toggle navigation Menu. Home Dictionary Tags Development.