If one had to identify one idea as central and novel to reinforcement learning, it would undoubtedly be temporal-difference TD learning. I love studying artificial intelligence concepts while correlating them to psychology — Human behaviour and the brain.
Reinforcement learning is no exception. Our topic of interest — Temporal difference was a term coined by Richard S. To understand the psychological aspects of temporal difference we need to understand the famous experiment — Pavlovian or Classical Conditioning. Ivan Pavlov performed a series of experiments with dogs. A set of dogs were surgically modified so that their saliva could be measured. These dogs were presented with food unconditioned stimulus — US in response to which excretion of saliva was observed unconditioned response — UR.
This is stimulus-response pair is natural and thus conditioned. Now, another stimulus was added. Right before presenting the food a bell was rung. The sound of bell is a conditioned stimulus CS. Because this CS was presented to the dog right before the US, after a while it was observed that the dog started salivating at the sound of the bell.
This response was called the conditioned response CR. Effectively, Pavolov was successful to make the dog salivate on the sound of bell. An amusing representation of this experiment was shown in the sitcom — The Office.
Based on ISI the whole experiment can be divided into types:. In the series of experiments, it was observed that a lower value of ISI showed a faster and more evident response salivating of dog while a longer ISI showed a weaker response. By this, we can conclude that to reinforce a stimulus-response pair the interval between the conditioned and unconditioned stimuli shall be less. This forms the basis of the Temporal Difference learning algorithm.
Model-dependent RL algorithms namely value and policy iterations work with the help of a transition table. A transition table can be thought of as a life hack book which has all the knowledge the agent needs to be successful in the world it exists in. Naturally, writing such a book is very tedious and impossible in most cases which is why model dependent learning algorithms have little practical use.
Reinforcement learning: Temporal-Difference, SARSA, Q-Learning & Expected SARSA in python
Temporal Difference is a model-free reinforcement learning algorithm. This means that the agent learns through actual experience rather than dw1560 ubuntu a readily available all-knowing-hack-book transition table. This enables us to introduce stochastic elements and large sequences of state-action pairs. The agent has no idea about the reward and transition systems. It does not know what will happen on taking an arbitrary action at an arbitrary state.
Temporal Difference algorithms enable the agent to learn through every single action it takes. TD updates the knowledge of the agent on every timestep action rather than on every episode reaching the goal or end state. The value Target-OldEstimate is called the target error. Its value lies between 0 and 1. The equation above helps us achieve Target by making updates at every timestep. Target is the utility of a state. Higher utility means a better state for the agent to transition into. For the sake of brevity of this post, I have assumed the readers know about the Bellman equation.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
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This is a personnal exercise to learn how to program basic reinforcement learning algorithm. Skip to content.
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You signed out in another tab or window.The essence of reinforcement learning is the way the agent iteratively updates its estimation of state, action pairs by trials if you are not familiar with value iteration, please check my previous example. In previous posts, I have been repetitively talking about Q-learning and how the agent updates its Q-value based on this method. In fact, besides the update method defined in Q-learning, there are more other ways of updating estimations of state, action pairs.
And it is from this temporal difference that our agent learns and updates itself. The definition of temporal difference distinguishes methods from each other.
It is clear that the only difference lies in updating the Q function. SARSA is also called on-policy, because the update process is consistent with the current policy. However the temporal difference defined in Q-learning is:. The Q-learning always uses the max value of next state, in which case the state, action being taken to update the Q value may not be consistent with the current policy, thus it is called off-policy method.
The difference, in fact, could result in different behaviours of an agent. The gut feeling is that Q-learning off-policy is more optimistic in value estimation, where it always assume the best action be taken in the process, which, could result in bolder actions of the agent. Whereas SARSA off-policy is more conservative in value estimation, which result in saver actions of the agent.
This is a standard un-discounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked Cliff. Stepping into this region incurs a reward of optimal path and sends the agent instantly back to the start.
This is a typical 2 dimensional board game, so the board settings are mostly same as the example I described here.
In a nut shell, we will have a Cliff class which represents the board that is able to:. These are the major function inside class Cliff. The giveReward function gives reward -1 to all states except the cliff area, where results in reward The agent starts at the left end of the board with a sign Sand the only way to end the game is to reach the right end of the board with a sign G.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I understand that the general "learning" step takes the form of:.
Where L is the learning rate, r is the reward associated to a,sQ s',a' is the expected reward from an action a' in the new state s' and D is the discount factor. Firstly, I don't undersand the role of the term - Q a,swhy are we re-subtracting the current Q-value? Secondly, when picking actions a and a' why do these have to be random?
I believe this is Epsilon-Greedy? Why not to this also to pick which Q a,s value to update? Or why not update all Q a,s for the current s? Why, say, not also look into an hypothetical Q s'',a''? I guess overall my questions boil down to what makes SARSA better than another breath-first or depth-first search algorithm?
Why do we subtract Q a,s? In theory, this is the value that Q a,s should be set to. However, we won't always take the same action after getting to state s from action aand the rewards associated with going to future states will change in the future.
Instead, we just want to push it in the right direction so that it will eventually converge on the right value. This is the amount that we would need to change Q a,s by in order to make it perfectly match the reward that we just observed.
Since we don't want to do that all at once we don't know if this is always going to be the best optionwe multiply this error term by the learning rate, land add this value to Q a,s for a more gradual convergence on the correct value. Why do we pick actions randomly? The reason to not always pick the next state or action in a deterministic way is basically that our guess about which state is best might be wrong. We put non-zero values into the table by exploring those areas of state space and finding that there are rewards associated with them.
As a result, something not terrible that we have explored will look like a better option than something that we haven't explored. Maybe it is. But maybe the thing that we haven't explored yet is actually way better than we've already seen. This is called the exploration vs exploitation problem - if we just keep doing things that we know work, we may never find the best solution. Choosing next steps randomly ensures that we see more of our options.Intelligent agent to play carrom game using Reinforcement learning and Deep neural network.
Q-learning agent to solve the frozen lake problem from the OpenAI gym. Implementing Reinforcement learning algorithms consodering graph data structures as MDP.
Solve the windy gridworld problem assuming eight possible actions, including the diagonal moves. Creating an AI capable of commanding troops and comparing different methods. Add a description, image, and links to the sarsa topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the sarsa topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 70 public repositories matching this topic Language: Python Filter by language.
Sort options. Star 0. Code Issues Pull requests. Updated Jan 16, Python. Updated Aug 21, Python. Updated May 8, Python. Updated Apr 25, Python. Updated Aug 1, Python. Updated Nov 5, Python. Updated Apr 5, Python. Updated Jun 27, Python. Updated May 23, Python. Updated Nov 8, Python. Updated Dec 31, Python. Updated May 5, Python. Updated Nov 28, Python. Updated Oct 9, Python.It can be proven that given sufficient training under any -soft policy, the algorithm converges with probability 1 to a close approximation of the action-value function for an arbitrary target policy.
Q-Learning learns the optimal policy even when actions are selected according to a more exploratory or even random policy. The procedural form of the algorithm is:.
This procedural approach can be translated into plain english steps as follows: Initialize the Q-values table, Q s, a. Observe the current state, s. Choose an action, afor that state based on one of the action selection policies explained here on the previous page -soft, -greedy or softmax. Take the action, and observe the reward, ras well as the new state, s'.
Update the Q-value for the state using the observed reward and the maximum reward possible for the next state. The updating is done according to the forumla and parameters described above. Set the state to the new state, and repeat the process until a terminal state is reached. The major difference between it and Q-Learning, is that the maximum reward for the next state is not necessarily used for updating the Q-values.
Instead, a new action, and therefore reward, is selected using the same policy that determined the original action. The name Sarsa actually comes from the fact that the updates are done using the quintuple Q s, a, r, s', a'. Where: s, a are the original state and action, r is the reward observed in the following state and s', a' are the new state-action pair.
The procedural form of Sarsa algorithm is comparable to that of Q-Learning:. As you can see, there are two action selection steps needed, for determining the next state-action pair along with the first.
The parameters and have the same meaning as they do in Q-Learning. To highlight the difference between Q-Learning and Sarsa, an example from  will be used. They took the cliff world shown below: The world consists of a small grid.
The goal-state of the world is the square marked G on the lower right-hand corner, and the start is the S square in the lower left-hand corner. There is a reward of negative associated with moving off the cliff and negative 1 when in the top row of the world.
Q-Learning correctly learns the optimal path along the edge of the cliff, but falls off every now and then due to the -greedy action selection. Sarsa learns the safe path, along the top row of the grid because it takes the action selection method into account when learning.
Because Sarsa learns the safe path, it actually receives a higher average reward per trial than Q-Learning even though it does not walk the optimal path. Here is a graph showing the reward per trial for both Sarsa and Q-Learning:. Source Code.State—action—reward—state—action SARSA is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.
This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent " S 1 ", the action the agent chooses " A 1 ", the reward " R " the agent gets for choosing this action, the state " S 2 " that the agent enters after taking that action, and finally the next action " A 2 " the agent chooses in its new state. The rest of the article uses the former convention. A SARSA agent interacts with the environment and updates the policy based on actions taken, hence this is known as an on-policy learning algorithm.
The Q value for a state-action is updated by an error, adjusted by the learning rate alpha. Q values represent the possible reward received in the next time step for taking action a in state splus the discounted future reward received from the next state-action observation. The learning rate determines to what extent newly acquired information overrides old information. A factor of 0 will make the agent not learn anything, while a factor of 1 would make the agent consider only the most recent information.
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The discount factor determines the importance of future rewards. A factor of 0 makes the agent "opportunistic" by only considering current rewards, while a factor approaching 1 will make it strive for a long-term high reward.
Since SARSA is an iterative algorithm, it implicitly assumes an initial condition before the first update occurs. A low infinite initial value, also known as "optimistic initial conditions",  can encourage exploration: no matter what action takes place, the update rule causes it to have higher values than the other alternative, thus increasing their choice probability. In it was suggested that the first reward r could be used to reset the initial conditions.
According to this idea, the first time an action is taken the reward is used to set the value of Q. This allows immediate learning in case of fixed deterministic rewards. This resetting-of-initial-conditions RIC approach seems to be consistent with human behavior in repeated binary choice experiments. From Wikipedia, the free encyclopedia. For other uses, see Sarsa.
Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural network.