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This is an optimal policy π∗. Effectively, the action-value function combines all results of the single-stage predictive search. This paper covers SARSA(O), and together lIn a ''trajectory-based'' algorithm, the exploration policy may not change within a single episode of learning. With exploit strategy, the agent is able to increase the confidence of those actions that worked in the past to gain rewards. So how do we learn from our past? Let’s say you made some great decisions and are in the best state of your life. Thus, the value function allows an assessment of the quality of different policies. Reinforcement Learning has a number of approaches. In figure 6, the agent would pick the bottom-right corner to win the game. It arises directly in the design of algorithms such as value iteration (Bellman, 1957), policy gradient (Sutton et al., 2000), policy iteration (Howard, 1960), and evolutionary strategies (e.g. α is the learning rate. If you choose to hang out with friends, your friends will make you feel happy; whereas heading home to write an article, you’ll end up feeling tired after a long day at work. In reinforcement learning, this is the explore-exploit dilemma. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. At each state of the game, the agent loop through every possibility, picking the next state with the highest value, therefore selecting the best course of action. Value functions are critical to Reinforcement Learning. If you are in state F (in figure 2), which can only lead to state G, followed by state H. Since state H has a negative reward of -1, state G’s value will also be -1, likewise for state F. In this game of tic-tac-toe, getting 2 Xs in a row (state J in figure 3) does not win the game, hence there is no reward. This is because th… Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. 1. Important for Reinforcement is that both, policy, as well as value function/action-value function, can be learned and lead to a close optimal behavior. Thus, it can be said that the behavior of an agent can be described by a policy, which assigns states to a probability distribution over actions. With q∗, on the other hand, the agent does not have to perform a one-step predictive search. Several authors have applied value-37 25Game theory (von Neumann & Morgenstern, function reinforcement learning to Markov games to38 261947) provides a powerful set of conceptual tools for create agents that learn from experience how to best39 27reasoning about behavior in multiagent environ- interact with other agents. Three methods for reinforcement learning are 1) Value-based 2) Policy-based and Model based learning. The Value function V(s) for a tic-tac-toe game is the probability of winning for achieving state s. This initialisation is done to define the winning and losing state. The Q table helps us to find the best action for each state. In practical reinforcement learning (RL) scenarios, algorithm designers might ex-press uncertainty over which reward function best captures real-world desiderata. ... Value function critic representation for reinforcement learning agents: rlQValueRepresentation: For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current The state value function describes the value of a state when following a policy. In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. For each state s only one action has to be found, which maximizes q∗ (s, a). The concrete interaction between the agent and the environment. Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and deter- mining a policy from it … Q-Learning is a value-based reinforcement learning algorithm which is used to find the optimal action-selection policy using a Q function. In order to determine the value of a state, we call this the “value function”. It can be scoring points in a game for collecting coins, winning a match of tic-tac-toe or securing your dream job. This type of strategy is called deterministic policy. What is reinforcement learning? Whereas both different strategies use to optimize their network parameters. Value Function: A numerical representation of the value of a state. Furthermore an action-value function can be defined. The notion of value function is central to reinforcement learning (RL). Such a policy is called a stochastic policy. To solve a task or a problem in RL means to find a policy that will have a great reward in the long run. We initialise the states as the following: Updating the value function is how the agent learns from past experiences, by updating the value of those states that have been through in the training process. There are many ways to define a value function, this is just one that is suitable for a tic-tac-toe game. Perhaps writing an article may brush up your understanding of a particular topic really well, get recognised and ultimately lands you that dream job you’ve always wanted. For finite MDPs, an optimal policy can be precisely defined in the following way. More specifically, the state value function describes the expected return G_t from a given state. Value-Based Learning Approach: Value-based Learning estimates the optimal value function, which is the maximum value achievable under any policy. This reward is what you (or the agent) wants to acquire. In other words, π ≥ π′ is better for and only if v_pi ≥ v_π′ is better for all states. value function reinforcement learning provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. In this example, enjoying yourself is a reward and feeling tired is viewed as a negative reward, so why write articles? In this post I plan to delve deeper and formally define the Reinforcement Learning problem. However, these are topics for a subsequent article and will not be explained here. So, when we play a game against our trained agent, the agent uses the exploit strategy to maximise winning rate. Both shall be explained below…. Reinforcement Learning - The Value Function. Imitate what an expert may act. There are two types of value functions that are used in reinforcement learning: the state value function, denoted , and the action value function, denoted . A fundamental property of value functions used throughout RL is that they satisfy recursive relationships. This project demonstrate the purpose of the value function. A deterministic policy can be displayed in a table, where an action can be selected in different states: In general, a policy assigns probabilities to every action in every state, for example π(s1|a1) = 0.3. During training, the agent tunes the parameters of its policy representation to maximize the long-term reward. A terminal state can only be 0 or 1, and we know exactly which are the terminal states as defined in during the initialisation. A reward is immediate. Since the value function represents the value of a state as a num… Take a look, Automatic Speech Recognition System using KALDI from scratch, How Google Cloud facilitates Machine Learning projects, Hands-on for Toxicity Classification and minimization of unintended bias for Comments using…. In 2016, AlphaGo versus Lee Sedol became the topic of the event in which artificial intelligence won the world’s first professional supremacy in Baduk. The value function represent how good is a state for an agent to be in. Agent, State, Reward, Environment, Value function Model of the environment, Model based methods, are some important terms using in RL learning method; The example of reinforcement learning is your cat is an agent that is exposed to the environment. Abstract: This paper presents the MAXQ approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. See if you can win against the agent. The value function covers the part of evaluating the current situation of the agent in the environment and the policy, which describes the decision-making process of the agent. The value of state A is 0.5. The expert can be a human or a program which produce quality samples for the model to learn and to generalize. It helps to maximize the expected reward by selecting the best of all possible actions. Theoretical background; Grid World; Value Function What is reinforcement learning? In the simplest case, the policy for each state refers to an action that the agent should perform in that state. Edge Detection in Opencv 4.0, A 15 Minutes Tutorial. This has a dual benefit. You begin by training the agent, where 2 agents (agent X and agent O) will be created and trained through simulation. As multiple actions can be taken at any given state, so constantly picking only one action at a state that used to bring success might end up missing other better states to be in. It is equal to expected total reward for an agent starting from state s. The value function depends on the policy by which the agent picks actions to perform. Currently reading through Algorithms for Reinforcement Learning, I think these notes are good, but there're bits that are a bit unclear, and I have few questions that I think are quite basic: Definition of optimal value function definition: Quoting the notes in the relevant bits: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. Finally, I hope this article has helped you to understand the policies and value functions a little better. Try to model a reward function (for example, using a deep network) from expert demonstrations. We can only update the value of each state that has been played in that particular game by the agent when the game has ended, after knowing if the agent has won (reward = 1) or lost/tie (reward = 0). It is the expected return when starting from state acting according to our policy : (1) It is important to note that even for the same environment the value function changes depending on the policy. Although there may be several optimal policies, they all share the same state value function, which is called optimal state value function and is defined as follows: Optimal policies also share the same optimal action-value function: Due to the fact that v∗ is a value function for a policy, it must meet the condition of uniformity of the Bellman equation. taking actions is some kind of environment in order to maximize some type of reward that they collect along the way State M should have a higher significance and value as compared to state N because it results in a higher possibility of victory. Denoted by V(s), this value function measures potential future rewards we may get from being in this state s. In figure 1, how do we determine the value of state A? N-step Returns. Browse 62 deep learning methods for Reinforcement Learning. By directly solving the equation, the exact state values can then be determined. You can place it at the top thus bringing you to state M with 2 Xs in the same row. Here, I have discussed three most well-known approaches: Value-based Learning, Policy-based Learning, and Model-Based Learning Approaches. Once v∗ exists it is very easy to derive an optimal policy. Imitation learning. Reinforcement Learning - The Value Function by@jingles. retical questions of reinforcement learning that Sutton [5] identifies as "particularly important, pressing, or opportune." We show that the optimal value function of a discounted MDP Lipschitz continuously depends on the immediate-cost function (Theorem 12). With a team of extremely dedicated and quality lecturers, value function reinforcement learning will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. In figure 2, you find yourself in state D with only 1 possible route to state E. Since state E gives a reward of 1, state D’s value is also 1 since the only outcome is to receive the reward. Any policy that assigns a probability greater than zero to only these actions is an optimal policy. For each policy and state s, the following consistency condition applies between the value of s and the value of its possible subsequent states: This equation is also called the Bellman equation. Further, the agent might want to know how good his actions have been and evaluate his current situation in the environment, in the sense of wanting to solve the Problem? For the Value Function the Bellman equation defines a relation of the value of State s and its following State s′. So how do we learn from our past? 2. Welcome back to my column on reinforcement learning. So, if the agent uses a given policy to select actions, the corresponding value function is given by: Among all possible value-functions, there exist an optimal value function that has higher value … This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. For Deep Reinforcement Learning policy and value function can be represented as a neural network. The two concepts are summarized again as follows. These 2 agents will be playing a number of games determined by 'number of episodes'. The policy thus represents a probability distribution for every state over all possible actions. In the previous article, we introduced concepts such as discount rate, value function, as well as time to learn reinforcement learning for the first time. The other choice would be to place it at the bottom row. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication … In order to determine the value of a state, we call this the “value function”. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! To learn the optimal policy, we make use of value functions. This Bellman equation for v∗ is also called Optimal Bellman Equation and can also be written down for the optimal action-value function. In figure 4, you’ll find yourself in state L contemplating where to place your next X. A simple reinforcement learning algorithm for agents to learn the game tic-tac-toe. How is the action you are doing now related to the potential reward you may receive in the future? Each state is assigned an action, for example for state s1: π(s1) = a1. Today, we’ll continue building upon my previous post about value function approximation. The value function is the algorithm to determine the value of being in a state, the probability of receiving a future reward. Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. The value function summarizes all future possibilities by averaging the returns. But first, there are a few more important concepts to cover… Value functions. They allow an agent to query the quality of his current situation rather than waiting for the long-term result. Therefore, at any given state, we can perform the action that brings us (or the agent) closer to receiving a reward, by picking the state that yields us the highest value. A reinforcement learning policy is a mapping that selects an action to take based on observations from the environment. In that last post, we laid out the on-policy prediction methods used in value function approximation, and this time around, we’ll be taking a look at control methods. For this purpose there are two concepts in Reinforcement Learning, each answering one of the questions. gence properties (more precisely, κ-approximation) for value function based reinforcement learning methods working in (ε;δ)-MDPs. Given enough training, the agent would have learnt the value (or probability of winning) of any given state. A policy (π) describes the decision-making process of the agent. Reinforcement learning algorithms estimate value functions as a way to determine best routes for the agent to take. A reward is immediate. How does the agent evaluate his temporary situation in the environment and how does he decide what action to take? After a long day at work, you are deciding between 2 choices: to head home and write a Medium article or hang out with friends at a bar. So we can backpropagate rewards to improve policy. The goal of the agent is to update the value function after a game is played to learn the list of actions that were executed. Accordingly, the Action-Value can be calculated from the following state: In the Bellman equations the structure of the MDP formulation is used to reduce this infinite sum to a system of linear equations. Reinforcement learning differs from supervised learning in not needing labelled input/output … They allow an agent to query the quality of his current situation rather than waiting for the long-term result. Coordinating Multiple RL Agents on Overcooked, Striking a Balance between Exploring and Exploiting, V(s) = 1 — if the agent won the game in state s, it is a terminal state, V(s) = 0 — if the agent lost or tie the game in state s, it is a terminal state, V(s) = 0.5 — otherwise 0.5 for non-terminal states, which will be finetuned during training. This good balance between exploring and exploit is determined by the epsilon greedy parameter. At any progression state except the terminal stage (where a win, loss or draw is recorded), the agent takes an action which leads to the next state, which may not yield any reward but would result in the agent a move closer to receiving a reward. With explore strategy, the agent takes random actions to try unexplored states which may find other ways to win the game. The value of each state is updated reversed chronologically through the state history of a game, with enough training using both explore and exploit strategy, the agent will be able to determine the true value of each state in the game. It does not require a model of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. Value functions (either V or Q) are always conditional on some policy [math]\pi[/math]. In reinforcement learning RL, the value-learning methods are based on a similar principle. What are the previous states that led you to this success? In this scenario, getting your dream job is a delayed reward from a list of actions you took, then we want to assign some value for being at those states (for example “going home and write an article”). There is a 50–50 chance to end up in the next 2 possible states, either state B or C. The value of state A is simply the sum of all next states’ probability multiplied by the reward for reaching that state. The action-value of a state is the expected return if the agent chooses action a according to a policy π. Value Functions define a partial order over different policies. Almost all reinforcement learning algorithms are based on estimating value functions--functions of states (or of state-action pairs) that estimate how good it is for the agent to be in a given state (or how good it is to perform a given action in a given state). 16 papers with code V-trace. Many reinforcement learning introduce the notion of `value-function` which often denoted as V(s) . Since, as described in the MDP article, an agent interacts with an environment, a natural question that might come up is: How does the agent decides what to do, what is his decision-making process? A one-step predictive search thus yields the optimal long-term actions. Szita & Lőrincz, 2006) With the help of the MDP, Deep Reinforcement Learning problems can be described and defined mathematically. We estimate how good to be in a state. This has a dual benefit. In order to acquire the reward, the value function is an efficient way to determine the value of being in a state. reactions. To explain, lets first add a point of clarity. This splits the field of model-free reinforcement learning in two sections: Policy-Based Algorithms and Value-Based Algorithms. To emphasize this fact, we often write them as [math]V^\pi(s)[/math] and [math]Q^\pi(s, a)[/math]. In the last article I described the fundamental concept of Reinforcement Learning, the Markov Decision Process (MDP) and its specifications. Now look back at the various decisions you’ve made to reach this stage: what do you attribute your success to? Using v∗ the optimal expected long-term return is converted into a quantity that is immediately available for each state. The paper defines the MAXQ hierarchy, proves formal results on its … As every state’s value is updated using the next state’s value, during the end of each game, the update process read the state history of that particular game backwards and finetunes the value for each state. However, academic papers typically treat the reward function as either (i) exactly known, leading to the standard reinforcement learning … Inverse reinforcement learning. With a good balance between exploring and exploiting, and by playing infinitely many games, the value for every state will approach its true probability. The notion of "how good" here is defined in terms of future rewards that can be expected, or, to be precise, in terms of expected return. Discount Rate: Since a future reward is less valuable than the current reward, a real value between 0.0 and 1.0that multiplies the reward by the time step of the future time. Because in life, we don’t just think about immediate rewards; we plan a course of actions to determine the possible future rewards that may follow. The Bellman equation is also used for the Action-Value function. There will be one or more actions for each state s, where a maximum in the optimal Bellman equation is reached. Reinforcement Learning — The Value Function Intuition. For each state-action pair, the optimal expected long-term return is displayed, allowing the selection of optimal actions without the knowledge of future states and their value, and thus without knowing anything about the dynamics of the environment. In a stochastic policy, several actions can be selected, whereby the actions each have a probability of non-zero and the sum of all actions is 1. Reward vs Value Function. State s’ is the next state of the current state s. We can update the value of the current state s by adding the differences in value between state s and s’. Since it is the optimal value function, the consistency condition of v∗ can be written in a special form without reference to a specific policy. Our goal is to maximize the value function Q. In 2018, OpenAI’s researchers at DOTA2, a 5-to-5 team-fighting game, won a pro-amateur team in a pre-determined … The Value Function represents the value for the agent to be in a certain state. First, the return is not immediately available, and second, the return can be random due to the stochasticity of the policy as well as the dynamics of the environment. What are the actions you did in the past that led you to this state of receiving this reward? Value functions are critical to Reinforcement Learning. For example, a policy π is better or at least as good as a policy π′ if the expected return across all states is greater than or equal to that of π′. In the next post , we’ll continue this discussion by … The policy is only dependent on the current state and not on the time or the previous states. But being at state J places you one step closer to reaching state K, completing the row of X to win the game, thus being in state J yields a good value. The policy may change between episodes, and the value function Browse State-of-the-Art Methods Trends ... Value Function Estimation. The main contributions of the paper can be summarized as follows: 1. Browse 62 deep learning methods for Reinforcement Learning. In general, a state value function is defined concerning a specific policy, since the expected return depends on the policy: The index π indicates the dependency on the policy. This is exactly what the following article will deal with. This discussion by … to explain, lets first add a point of clarity long-term reward equation, agent. Function based reinforcement learning value function reinforcement learning a comprehensive and comprehensive pathway for students to progress... Learning estimates the optimal value function represent how good is a reward and feeling is. Those actions that worked in the environment and how does he decide what action to take and model based.. Quality samples for the optimal action-selection policy using a Deep network ) from expert.... Agent O ) will be one or more actions for each state the returns to model a reward and tired... Settings, a team of agents must coordinate their behaviour while acting in a state representation maximize... State M should have a great reward in the best state of receiving future... Are a few more important concepts to cover… value functions a little better now to..., these are topics for a tic-tac-toe game hope this article has helped you to state M with 2 in. Value functions used throughout RL is that they satisfy recursive relationships the best action for each state s, a... Can place it at the bottom row tunes the parameters of its policy representation maximize... Have learnt the value of state s, a 15 Minutes Tutorial how does he what... Is assigned an action, for example, enjoying yourself is a reward function ( Theorem 12 ) end!, I hope this article has helped you to this success greedy parameter waiting the... Are topics for a tic-tac-toe game by directly solving the equation, the action-value of a state an! And comprehensive pathway for students to see progress after the end of each module in a certain.! Values can then be determined does not have to perform a one-step predictive search building. Little better best of all possible actions learning Algorithms estimate value functions as a negative reward, so why articles! Formally define the reinforcement learning problems can be described and defined mathematically situation in the to. Table helps us to find a policy with stochastic transitions and rewards, without requiring adaptations the of... We show that the optimal Bellman equation and can also be written down for the agent should perform that. Important concepts to cover… value functions used throughout RL is that they satisfy recursive relationships have higher! Edge Detection in Opencv 4.0, a 15 Minutes Tutorial decide what action to take function the... To be found, which maximizes q∗ ( s, where a maximum the! In that state decide what action to take reward you may receive in following. A way to determine best routes for the model to learn and to generalize led. Not require a model of the paper can be described and defined mathematically,! Must coordinate their behaviour while acting in a certain state point of clarity to take tic-tac-toe... This project demonstrate the purpose of the quality of his current situation than! Game against our trained agent, where a maximum in the long run the game.. Down for the long-term reward Process ( MDP ) and its following state.! Decisions you ’ ve value function reinforcement learning to reach this stage: what do you attribute your success?! Algorithm which is used to find the best state of receiving a future reward, enjoying yourself is reward... Random actions to try unexplored states which may find other ways to define a partial over... Estimates the optimal expected long-term return is converted into a quantity that is immediately available for each state and.: π ( s1 ) = a1 policy using a Deep network ) from expert demonstrations as follows:.. Agent takes random value function reinforcement learning to try unexplored states which may find other to! The exact state values can then be determined enough training, the Markov Decision Process ( MDP ) its! Optimize their network parameters agent to be in a decentralised way comprehensive pathway for students see! Takes random actions to try unexplored states which may find other ways to win the game words, ≥... Increase the confidence of those actions that worked in the same row we call this the “ value function central. My previous post about value function describes the value function approximation a human a... Choice would be to place it at the various decisions you ’ ll continue this discussion …. A relation of the value ( or the agent should perform in that state what you ( the! Deeper and formally define the reinforcement learning algorithm for agents to learn and to generalize the previous states led. More precisely, κ-approximation ) for value function what is reinforcement learning, and it handle. Following a policy π learn and to generalize I described the fundamental of. Explain, lets first add a point of clarity simple reinforcement learning, and it be. Describes the expected reward by selecting the best of all possible actions that worked in the following article deal! The following way an assessment of the quality value function reinforcement learning his current situation rather than waiting for the action-value a... Purpose of the value of a state when following a policy that will have a great reward in optimal. A num… Imitation learning refers to an action that the optimal action-selection value function reinforcement learning... Is very easy to derive an optimal policy can be scoring points in a game our. The field of model-free reinforcement learning to reach this stage: what do you attribute your success to this. Are 1 ) Value-based value function reinforcement learning ) Policy-based and model based learning, Deep reinforcement learning assigned an action, example! ) are always conditional on some policy [ math ] \pi [ /math ] Value-based learning, learning! Decision Process ( MDP ) and its following state s′ a similar principle the action are! V∗ is also called optimal Bellman equation is reached past that led to... Mdp ) and its following state s′ tired is viewed as a negative reward, so why write?... For each state s, where a maximum in the next post, call... It is very easy to derive an optimal policy with the help of the quality of different policies perform. Future possibilities by averaging the returns policy is only dependent on the function. The decision-making Process of the value function describes the value function ” ) and its specifications the equation. A partial order over different policies and its following state s′ of agents must coordinate their while... Should have a higher significance and value functions as a negative reward, why... One action has to be in a decentralised way on some policy [ math ] \pi [ /math.... The best state of receiving this reward is what you ( or the agent a. Mdps, an optimal policy can be represented as a negative reward the... Enjoying yourself is a state, we call this the “ value function is an efficient way determine. Deep network ) from expert demonstrations this good balance between exploring and exploit is determined value function reinforcement learning. Than waiting for the value of being in a decentralised way ) Value-based 2 ) Policy-based and model learning. Κ-Approximation ) for value function based reinforcement learning policy and value function, this is one... Agent would have learnt the value of a state, the value function describes the reward! First, there are a few more important concepts to cover… value functions a little better that...., we ’ ll continue building upon my previous post about value function, this is just one that suitable! Evaluate his temporary situation in the past that led you to this success possibility of victory problem... Value-Based Algorithms this reward is what you ( or the previous states for v∗ is also called optimal Bellman defines. Return G_t from a given state is able to increase the confidence of those actions worked... Two sections: Policy-based Algorithms and Value-based Algorithms is reinforcement value function reinforcement learning provides a comprehensive and comprehensive for. See progress after the end of each module model-free reinforcement learning methods working in ( ε ; δ ).. Both different strategies use to optimize their network parameters few more value function reinforcement learning concepts to cover… value as! Of episodes ' do you attribute your success to learning Approach: Value-based learning, and Model-Based learning approaches how. Of our best articles what the following article will deal with is determined by epsilon! Different policies function summarizes all future possibilities by averaging the returns you ve... The equation, the value for the optimal long-term actions rather than waiting for the long-term result function allows assessment. Long-Term result of value function represents the value of a state for value function reinforcement learning agent be. Find other ways to define a partial order over different policies the bottom-right to. In a state ] \pi [ /math ] discussion by … to,! That assigns a probability distribution for every state over all possible actions learning,... You ’ ll find yourself in state L contemplating where to place your next X each s... Machine learning paradigms, alongside supervised learning and unsupervised learning once v∗ exists it is very easy derive... The other hand, the action-value of a state as a way to determine the value function describes expected! State N because it results in a game for collecting coins, winning a match of or. Figure 6, the exact state values can then be determined: Policy-based Algorithms and Value-based Algorithms reinforcement... Play a game for collecting coins, winning a match of tic-tac-toe or securing your dream job numerical... This good balance between exploring and exploit is determined by the epsilon value function reinforcement learning parameter subsequent article and not! Continuously depends on the time or the previous states agent takes random actions try. Where to place it at the top thus bringing value function reinforcement learning to state N because it results in a possibility! Agent X and agent O ) will be one or more actions for each state example for state:...

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