Lower epsilon value results in episodes with more penalties on average which is obvious because we are exploring and making random decisions.
We can now create the training algorithm that will update this Q-table as the agent explores the environment over thousands of episodes. In the first part of while not done , we decide whether to pick a random action or to exploit the already computed Q-values. This is done simply by using the epsilon value and comparing it to the random. Now that the Q-table has been established over , episodes, let's see what the Q-values are at our illustration's state:. The max Q-value is "north" Let's evaluate the performance of our agent.
We don't need to explore actions any further, so now the next action is always selected using the best Q-value:. With Q-learning agent commits errors initially during exploration but once it has explored enough seen most of the states , it can act wisely maximizing the rewards making smart moves. Let's see how much better our Q-learning solution is when compared to the agent making just random moves. These metrics were computed over episodes. And as the results show, our Q-learning agent nailed it!
Ideally, all three should decrease over time because as the agent continues to learn, it actually builds up more resilient priors;. We may want to track the number of penalties corresponding to the hyperparameter value combination as well because this can also be a deciding factor we don't want our smart agent to violate rules at the cost of reaching faster.
A more fancy way to get the right combination of hyperparameter values would be to use Genetic Algorithms. We began with understanding Reinforcement Learning with the help of real-world analogies. We then dived into the basics of Reinforcement Learning and framed a Self-driving cab as a Reinforcement Learning problem.
We then used OpenAI's Gym in python to provide us with a related environment, where we can develop our agent and evaluate it. Then we observed how terrible our agent was without using any algorithm to play the game, so we went ahead to implement the Q-learning algorithm from scratch. The agent's performance improved significantly after Q-learning. Finally, we discussed better approaches for deciding the hyperparameters for our algorithm. Q-learning is one of the easiest Reinforcement Learning algorithms. The problem with Q-earning however is, once the number of states in the environment are very high, it becomes difficult to implement them with Q table as the size would become very, very large.
State of the art techniques uses Deep neural networks instead of the Q-table Deep Reinforcement Learning. The neural network takes in state information and actions to the input layer and learns to output the right action over the time.
Reinforcement Q-Learning from Scratch in Python with OpenAI Gym
Deep learning techniques like Convolutional Neural Networks are also used to interpret the pixels on the screen and extract information out of the game like scores , and then letting the agent control the game. We have discussed a lot about Reinforcement Learning and games. But Reinforcement learning is not just limited to games. It is used for managing stock portfolios and finances, for making humanoid robots, for manufacturing and inventory management, to develop general AI agents, which are agents that can perform multiple things with a single algorithm, like the same agent playing multiple Atari games.
Open AI also has a platform called universe for measuring and training an AI's general intelligence across myriads of games, websites and other general applications. Software Developer experienced with Data Science and Decentralized Applications, having a profound interest in writing. Toggle navigation flattened-logo-ready-for-export. You are reading Tutorials. Be notified when we release new material Join over 3, data science enthusiasts.
Author: Satwik Kansal Software Developer. Before Tutorial. After Tutorial. Reinforcement Learning Analogy. That's exactly how Reinforcement Learning works in a broader sense: Your dog is an "agent" that is exposed to the environment. The environment could in your house, with you. The situations they encounter are analogous to a state. An example of a state could be your dog standing and you use a specific word in a certain tone in your living room Our agents react by performing an action to transition from one "state" to another "state," your dog goes from standing to sitting, for example.
After the transition, they may receive a reward or penalty in return. You give them a treat! Or a "No" as a penalty. The policy is the strategy of choosing an action given a state in expectation of better outcomes. Reinforcement Learning lies between the spectrum of Supervised Learning and Unsupervised Learning, and there's a few important things to note: Being greedy doesn't always work There are things that are easy to do for instant gratification, and there's things that provide long term rewards The goal is to not be greedy by looking for the quick immediate rewards, but instead to optimize for maximum rewards over the whole training.
Sequence matters in Reinforcement Learning The reward agent does not just depend on the current state, but the entire history of states. Unlike supervised and unsupervised learning, time is important here. The Reinforcement Learning Process. Breaking it down, the process of Reinforcement Learning involves these simple steps: Observation of the environment Deciding how to act using some strategy Acting accordingly Receiving a reward or penalty Learning from the experiences and refining our strategy Iterate until an optimal strategy is found Let's now understand Reinforcement Learning by actually developing an agent to learn to play a game automatically on its own.
Want to learn more? Practical Reinforcement Learning Coursera. Example Design: Self-Driving Cab. Here are a few things that we'd love our Smartcab to take care of: Drop off the passenger to the right location. Save passenger's time by taking minimum time possible to drop off Take care of passenger's safety and traffic rules There are different aspects that need to be considered here while modeling an RL solution to this problem: rewards, states, and actions.
Here a few points to consider: The agent should receive a high positive reward for a successful dropoff because this behavior is highly desired The agent should be penalized if it tries to drop off a passenger in wrong locations The agent should get a slight negative reward for not making it to the destination after every time-step.
In other words, we have six possible actions: south north east west pickup dropoff This is the action space : the set of all the actions that our agent can take in a given state. Implementation with Python. Once installed, we can load the game environment and render what it looks like:.
The following are the env methods that would be quite helpful to us: env. Returns observation : Observations of the environment reward : If your action was beneficial or not done : Indicates if we have successfully picked up and dropped off a passenger, also called one episode info : Additional info such as performance and latency for debugging purposes env.
Here's our restructured problem statement from Gym docs : "There are 4 locations labeled by different letters , and our job is to pick up the passenger at one location and drop him off at another. The filled square represents the taxi, which is yellow without a passenger and green with a passenger. The pipe " " represents a wall which the taxi cannot cross. R, G, Y, B are the possible pickup and destination locations.
The blue letter represents the current passenger pick-up location, and the purple letter is the current destination. A few things to note: The corresponds to the actions south, north, east, west, pickup, dropoff the taxi can perform at our current state in the illustration. In this env, probability is always 1. If we are in a state where the taxi has a passenger and is on top of the right destination, we would see a reward of 20 at the dropoff action 5 done is used to tell us when we have successfully dropped off a passenger in the right location.
Each successfull dropoff is the end of an episode Note that if our agent chose to explore action two 2 in this state it would be going East into a wall.
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Solving the environment without Reinforcement Learning. Let's see what would happen if we try to brute-force our way to solving the problem without RL. Let's see what happens:. Enter Reinforcement Learning.
What is this saying? Summing up the Q-Learning Process. Breaking it down into steps, we get Initialize the Q-table by all zeros. Start exploring actions: For each state, select any one among all possible actions for the current state S. Travel to the next state S' as a result of that action a.
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For all possible actions from the state S' select the one with the highest Q-value. Update Q-table values using the equation. Set the next state as the current state.
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If goal state is reached, then end and repeat the process. Implementing Q-learning in python. Comparing our Q-learning agent to no Reinforcement Learning. We evaluate our agents according to the following metrics, Average number of penalties per episode: The smaller the number, the better the performance of our agent. Ideally, we would like this metric to be zero or very close to zero. Average number of timesteps per trip: We want a small number of timesteps per episode as well since we want our agent to take minimum steps i.
Average rewards per move: The larger the reward means the agent is doing the right thing. That's why deciding rewards is a crucial part of Reinforcement Learning. In our case, as both timesteps and penalties are negatively rewarded, a higher average reward would mean that the agent reaches the destination as fast as possible with the least penalties".
Measure Random agent's performance Q-learning agent's performance Average rewards per move Hyperparameters and optimizations. Tuning the hyperparameters. Conclusion and What's Ahead. Course Recommendations. Lots of great notebooks with hands-on exercises. Goes through more advanced Q-Learning techniques and math.
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