### Frozen Lake: Beginners Guide To Reinforcement Learning With OpenAI Gym

You can run the code for this section in this jupyter notebook link. The key here is we want to get to G without falling into the hole H in the shortest amount of time.

In this game, we know our transition probability function and reward function, essentially the whole environment, allowing us to turn this game into a simple planning problem via dynamic programming through 4 simple functions: 1 policy evaluation 2 policy improvement 3 policy iteration or 4 value iteration. Deep Learning Wizard. Dynamic Programming Run Jupyter Notebook You can run the code for this section in this jupyter notebook link.

Observation space State space print env. Sampling state space We should expect to see 15 possible grids from 0 to 15 when we uniformly randomly sample from our observation space for i in range 10 : print env. Action space Action space print env. Random sampling of actions We should expect to see 4 actions when we uniformly randomly sample: 1. LEFT: 0 2. Initial state This sets the initial state at S, our starting point We can render the environment to see where we are on the 4x4 frozenlake gridworld env.

Go right? Go right 10 times? Intuitively when we are moving on a frozen lake, some times when we want to walk one direction we may end up in another direction as it's slippery Setting seed here of the environment so you can reproduce my results, otherwise stochastic policy will yield different results for each run env.

DiscreteEnv : """ Winter is here. You and your friends were tossing around a frisbee at the park when you made a wild throw that left the frisbee out in the middle of the lake. The water is mostly frozen, but there are a few holes where the ice has melted.

If you step into one of those holes, you'll fall into the freezing water. At this time, there's an international frisbee shortage, so it's absolutely imperative that you navigate across the lake and retrieve the disc. However, the ice is slippery, so you won't always move in the direction you intend. You receive a reward of 1 if you reach the goal, and zero otherwise.

Probability 1. Policy plot import seaborn as sns import matplotlib. Returns V comprising values of states under given policy. Args: env gym. The new value of the state is smaller than a tiny positive value we set State value change is tiny compared to what we have so we just stop! This is our environment Notice how the state values near the goal have higher values? For each state in 16 states for s in range env. Compared to this equiprobable policy, the one above is making some improvements by maximizing q-values per state plt.

State values plt. State values without policy improvement, just evaluation plt.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. For example:. The P attribute will be the most important for your implementation of value iteration and policy iteration. This attribute contains the model for the particular map instance. It is a dictionary of dictionary of lists with the following form:. For example, to get the probability of taking action LEFT in state 0 you would use the following code:.

This would return the list: [ 1. There is one tuple in the list, so there is only one possible next state. The next state will be state 0, according to the second number in the tuple. The final tuple value says that the next state is not terminal.

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### Value Iteration

LEFT will print out the number 0. Environment Attributes This class contains the following important attributes: nS :: number of states nA :: number of actions P :: transitions, rewards, terminals The P attribute will be the most important for your implementation of value iteration and policy iteration. LEFT] This would return the list: [ 1. Running a random policy example. Value Iteration The optimal policies for the different environments is in the. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window. Feb 24, This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.

You should have a background in statistics expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions and foundational knowledge of financial markets equities, bonds, derivatives, market structure, hedging. It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies.

Thank you so much. Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn. In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. Loupe Copy. Reinforcement Learning for Trading Strategies.

Course 3 of 3 in the Machine Learning for Trading Specialization. Enroll for Free. From the lesson. Introduction to Course and Reinforcement Learning. History Overview Value Iteration Policy Iteration Taught By. Jack Farmer Curriculum Director. Ram Seshadri Machine Learning Consultant.

Try the Course for Free. Explore our Catalog Join for free and get personalized recommendations, updates and offers. Get Started. All rights reserved.In this article, we describe how genetic algorithm can be used to solve reinforcement learning problem. These methods are regarded as a meta-heuristic optimization method which means that they can be useful for find good solutions for optimization maximization or minimization problems, but they do not provide guarantees of finding the global optimal solution.

Solving a problem by using genetic algorithm require representing its solution as a string of chromosomes e. A genetic algorithm works by maintaining a pool of candidate solutions named generation. Iteratively, the generation evolved to produce the next generation which has candidate solutions with higher fitness values than the previous generation. This process is repeated for a pre-specified number of generations or until a solution with goal fitness value is found.

The next generation is created from current generation in a biologically inspired manner that consists of 3 steps:. Imagine you are standing on top of a frozen lake.

Your initial position is marked as S and you want to reach the position marked by G. However, if you step into any position marked as H you will fall into the water and there is no return back.

Moreover, the surface of the lake is slippery so your actions of movement may not get executed as you want. For example, when you try to move forward, your actual move may be to the right or the left. Remember from our previous article, you can model reinforcement learning problems as Markov Decision Process. In this problem, both the set of actions and states are discrete sets.

The set of states S includes the possible locations in the grid we can number them by numbers from 1 to The set of actions A includes possibles moves: forward, backward, left or right. We can denote them as 1, 2, 3, 4. Solving this problem requires finding a policy i. Now, lets see if how far can a random search for a good policy achieve for this problem.

In this problem, we have 16 states and 4 possible moves. Of course, it is not feasible to evaluate all of them, but we can generate a random set of solutions and select the best among them. The script below generates a set of random solution and evaluates them.

## This Is How Reinforcement Learning Works

The best policy score we get is only around 0. To solve this problem by using genetic algorithm, we encode each solution as an array of 16 values which each value can be either 0,1,2, 3 representing the four possible moves at the different 16 positions. We generate an initial population of random solutions, and we iterate through 10 generations by doing selectioncrossoverand mutation.

As a result, we can see how the that best score in in the initial population is 0. Link of solution on OpenAI gym. Next time, we will explain the methods of value-iteration, and policy iteration and demonstrate how they can solve another example of reinforcement learning problems.

Sign in. Episode 1 — Genetic Algorithm for Reinforcement Learning. Moustafa Alzantot Follow. See responses 5. More From Medium. Discover Medium. Make Medium yours. Become a member. About Help Legal.In late Google introduced AlphaZeroan AI system that taught itself from scratch how to master the games of chess, Go and shogi in four hours.

The short training time was largely enough for AlphaZero to beat world champion chess programs. In our previous articlewe introduced the building blocks of Reinforcement Learning.

Now we will dive deeper into the mechanisms used by AI agents to teach themselves how to take the right flow of actions for achieving a globally rewarding objective. Some tiles of the grid are walkable, and others lead to the agent falling into the water. The agent is rewarded for finding a walkable path to a goal tile.

Even for such fairly simple environments, we can have a variety of policies. The agent can always move forward for example, or choose an action randomly or try to go around obstacles by checking whether that previous forward action failed, or even funnily spin around to entertain.

Different policies can give us different return, which makes it important to find a good policy. Formally, policy is defined as the probability distribution over actions for every possible state:. The value function V s is the expected long-term return with discount for state sas opposed to the short-term reward. The value function represents how good is a state for an agent to be in. It is equal to expected total reward for an agent starting from that state.

The value function depends on the policy by which the agent picks actions to perform. Learning the optimal policy requires us to use the so-called Bellman equation.

The agent can perform either the action 1, 2, … or N.

## Episode 1 — Genetic Algorithm for Reinforcement Learning

This would bring the agent to a future state S1, S2, … or SN. The agent would get the reward r1, r2, … or rN accordingly. The expected long-term reward for each future state would be V1, V2, … or VN. The optimal policy would help the agent to choose the best possible action. The above equation is called deterministic Bellman equation.

It can become a stochastic equation if, for a given action, the agent can reach more than one future state with different probabilities. This situation is illustrated below. The resulting stochastic Bellman equation for this general case is given as follows. We provide an implementation of the Bellman equation to choose the best possible action at a given state s. The function calculates the resulting values for every action and choose the maximum possible outcome.

You will need to load the necessary prerequisite libraries as described in our previous article. In the previous section, we explained how to find the best action that provides the maximal long-term value.

If we can do this for all states, we will obtain the value function. We will also know which action to perform at each state optimal policy. This algorithm is called value iteration. The value iteration algorithm randomly selects an initial value function.

It then calculates a new improved value function in an iterative process, until it reaches an optimal value function. Finally, it derives the optimal policy from that optimal value function.

The algorithm iterates until V[s] is not significantly improving anymore. The optimal policy P is then to take every time the action to go state with the highest V value.Reinforcement learning is a technique in building an artificial intelligent network where an agent is allowed to play or run by itself, correcting its movements and outputs every time it makes a mistake.

The computation power and training time required solely depends on the type of problem we are trying to solve by building a model. OpenAI gym is an environment where one can learn and implement the Reinforcement Learning algorithms to understand how they work. It gives us the access to teach the agent from understanding the situation by becoming an expert on how to walk through the specific task.

In this article, we will be working on the Frozen Lake environment where we teach the agent to move from one block to another and learn from the mistakes. In the Q-Learning method of reinforcement learning, the value is updated by an off-policy.

**Value Iteration in Deep Reinforcement Learning**

A greedy action is allowed during training which helps the agent explore the environment. Greedy action refers to letting a random action or movement to occur which then allows the agent to explore the unseen block.

The advantage of off-policy over on-policy is that the model will not get trapped at the local minima. The agent moves around the grid until it reaches the goal or the hole. If it falls into the hole, it has to start from the beginning and is rewarded the value 0. The process continues until it learns from every mistake and reaches the goal eventually. The agent in the environment has four possible moves — Up, Down, Left and Right. We will be implementing one of the Reinforcement Learning techniques, Q-Learning, here.

This environment will allow the agent to move accordingly. Considering this situation, we need to allow some random movement at first, but eventually try to reduce its probability. This way we can correct the error caused by minimising the loss. This grid has 16 possible blocks where the agent will be at a given time.

At the current state, the agent will have four possibilities of deciding the next state. These are our weights and we will update them according to the movement of our agent. Let us start with importing the libraries and defining the required placeholders and variables.

Once we have all the required resources, we can start training our agent to deal with the Frozen Lake situation. At first, the random movement allows the agent to move around and understand the environment.

Later, we will reduce this random action which allows the agent to move in the direction which is likely to be either a frozen state or the goal.

Every episode starts with the position Safe S and then the agent continues to move around the grid trying new blocks. The episode ends once the agent has reached the Goal for which the reward value is 1. This was the example of a simple Q-Learning technique which is an off-policy method. One can implement this method to train a model by unsupervised learning. Once the agent has learnt the environment, the model converges to a point where a random action is not required. We can trace the state at every iteration of every episode to see how the weights vary.

The key in the off-policy method is the allowance of the greedy action and the move to a next state. Since we allow this action, the agent will converge faster at the local minima and learn the environment sooner than an on-policy method. A Data Science Enthusiast who loves to read about the computational engineering and contribute towards the technology shaping our world.

He is a Data Scientist by day and Gamer by night.The agent controls the movement of a character in a grid world.

Some tiles of the grid are walkable, and others lead to the agent falling into the water. Additionally, the movement direction of the agent is uncertain and only partially depends on the chosen direction. The agent is rewarded for finding a walkable path to a goal tile. Winter is here.

You and your friends were tossing around a frisbee at the park when you made a wild throw that left the frisbee out in the middle of the lake. The water is mostly frozen, but there are a few holes where the ice has melted.

If you step into one of those holes, you'll fall into the freezing water. At this time, there's an international frisbee shortage, so it's absolutely imperative that you navigate across the lake and retrieve the disc. However, the ice is slippery, so you won't always move in the direction you intend. The episode ends when you reach the goal or fall in a hole. You receive a reward of 1 if you reach the goal, and zero otherwise.

FrozenLake-v0 The agent controls the movement of a character in a grid world. View source on GitHub.

RandomAgent on FrozenLake-v0.

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