I am trying to train a bot in a game like curve fever. It is like a snake which moves with a really precise turn radius (not 90°), which makes random hole (where he can passes throw) and like for a snake game he dies if he goes out of map or hits himself. The difference in the points stands on the fact that the snake has to survive as long as possible and there is no food associated. The tail of the snail increases by 1 at every step. It looks like that:
So I use a deep q learning algorithm with a CNN network, inspired by: Flappy bird deep q learning, which is itself inspired by the DeepMind's paper Playing Atari with Deep Reinforcement Learning.
My images as input are a thresholding image like above where everything is black or white.
At every step I grant +0.1 as reward for staying alive and -1 for dying in border of map or itself.
I trained my agent for hours and after 4.000.000 iterations I result in an agent which almost never goes out of map but crashes on itself in a very fast way.
So it is like he learnt how to not crash on border of the map but not on itself, what could explain this ?
Some examples:
My suppositions are:
I took a replay memory size of 25000 instead of 50000 because of OOM error, is that enough?
I did not train him long enough, but how could I know?
The border of the map never changes so it is easy to learn from it, but the tail of the agent itself changes at every new game, should I get worse reward for crashing on itself so the agent takes it more into account?
Here are my learning curves:
I am requesting your help because it takes a lot of time to train my agent and I can't be sure of what should I do.
Thanks in advance for any help.
I have trained an RL agent in an environment similar to the Puckworld. Theres no puck though! The agent is in continuous space and wants to reach a fixed target. Each episode the agent is born at a random location and there is an added noise to each action to make learning less trivial.
The reward is given every step as a scaled version of the distance to the target.
I want to plot the convergence of the neural network. The same problem in discrete space and using Q learning, I would plot the sum of all elements in Q matrix vs episode number. This gave me a good understanding of the performance of the network. How can i do the same for a neural network?
Plotting the reward collected in an episode vs episode number is not optimal here.
I use PyTorch. Any help is appreciated
I just read the paper of Mnih (2013) and was really wondering about the aspect that he talks about using RMSprop with minibatches of size 32 (page 6).
My understanding of these kinds of reinforcement learning algorithms is, that there is only 1 or at least very little amount of training samples per fit, and in every fit I update the network.
Whereas in supervised learning I have up to millions of samples and divide them in minibatches of e.g. 32 and update the network after every minibatch, which makes sense.
So my question is: If I put only one sample into the neural network at a time, how does minibatches make sense? Did I understand something wrong about that concept?
Thanks in advance!
The answer provided by Filip is correct. Just to add intuition to his answer, the reason why an experience replay is used is to decorrelate the experiences that the RL experienced. This is essential when non-linear function approximation is used such as neural networks.
Example: Imagine if you had 10 days to study for a chemistry and math test, and both test were on the same day. If you spend the first 5 days on chemistry and last 5 days on math, you would have forgotten most of the chemistry you studied. A neural network behaves similarly.
By decorrelating the experiences, a more general policy can be identified through the training data.
And while training the neural network, we have a batch of memory (i.e., data), and we sample random mini-batches of 32 from them to do supervised learning, just as any other neural network is trained.
The paper you mentioned introduces two mechanisms that stabilize Q-Learning method when used with a deep neural network function approximator. One of the mechanisms is called Experience Replay, and it is basically a memory buffer for observed experiences. You can find the description in the paper in the end of the fourth page. Instead of learning from the single experience you have just seen, you save it to the buffer. Learning is done every N iterations and you sample a random minibatch of experiences from the replay buffer.
So i wanna learn reinforcement learning by doing some examples. I wrote 2048 game but i do not know if i'm training it right. So as I understand I have to create neural network. I have created 16 inputs for each number. Then hidden layers 12x8 and 4 outputs for moves(up, right, down, left). (Activation function linear function for lat layer and relu for rest) Then I run one full game and save all the moves and rewards(0-nothing happend, -2-to moves that do nothink, -1 when that move lost game and a number of earned score when move do somethink). When the game ends I did backpropagation algorithm from the last move. Am i doing it rigth or what? And I know there are libraries like tensorflow but I wanna understand it all.
I would consult this GitHub repo, as it accomplishes exactly what you are trying to do.
You can actually use the above solution live here.
If you want to actually learn the fundamentals of how that all works, that's beyond the scope of what a single post on StackOverflow can provide.
I'm programming a software agent to control a robot player in a simulated game of soccer. Ultimately I hope to enter it in the RoboCup competition.
Amongst the various challenges involved in creating such an agent, the motion of it's body is one of the first I'm facing. The simulation I'm targeting uses a Nao robot body with 22 hinge to control. Six in each leg, four in each arm and two in the neck:
(source: sourceforge.net)
I have an interest in machine learning and believe there must be some techniques available to control this guy.
At any point in time, it is known:
The angle of all 22 hinges
The X,Y,Z output of an accelerometer located in the robot's chest
The X,Y,Z output of a gyroscope located in the robot's chest
The location of certain landmarks (corners, goals) via a camera in the robot's head
A vector for the force applied to the bottom of each foot, along with a vector giving the position of the force on the foot's sole
The types of tasks I'd like to achieve are:
Running in a straight line as fast as possible
Moving at a defined speed (that is, one function that handles fast and slow walking depending upon an additional input)
Walking backwards
Turning on the spot
Running along a simple curve
Stepping sideways
Jumping as high as possible and landing without falling over
Kicking a ball that's in front of your feet
Making 'subconscious' stabilising movements when subjected to unexpected forces (hit by ball or another player), ideally in tandem with one of the above
For each of these tasks I believe I could come up with a suitable fitness function, but not a set of training inputs with expected outputs. That is, any machine learning approach would need to offer unsupervised learning.
I've seen some examples in open-source projects of circular functions (sine waves) wired into each hinge's angle with differing amplitudes and phases. These seem to walk in straight lines ok, but they all look a bit clunky. It's not an approach that would work for all of the tasks I mention above though.
Some teams apparently use inverse kinematics, though I don't know much about that.
So, what approaches are there for robot biped locomotion/ambulation?
As an aside, I wrote and published a .NET library called TinMan that provides basic interaction with the soccer simulation server. It has a simple programming model for the sensors and actuators of the robot's 22 hinges.
You can read more about RoboCup's 3D Simulated Soccer League:
http://en.wikipedia.org/wiki/RoboCup_3D_Soccer_Simulation_League
http://simspark.sourceforge.net/wiki/index.php/Main_Page
http://code.google.com/p/tin-man/
There is a significant body of research literature on robot motion planning and robot locomotion.
General Robot Locomotion Control
For bipedal robots, there are at least two major approaches to robot design and control (whether the robot is simulated or physically real):
Zero Moment Point - a dynamics-based approach to locomotion stability and control.
Biologically-inspired locomotion - a control approach modeled after biological neural networks in mammals, insects, etc., that focuses on use of central pattern generators modified by other motor control programs/loops to control overall walking and maintain stability.
Motion Control for Bipedal Soccer Robot
There are really two aspects to handling the control issues for your simulated biped robot:
Basic walking and locomotion control
Task-oriented motion planning
The first part is just about handling the basic control issues for maintaining robot stability (assuming you are using some physics-based model with gravity), walking in a straight-line, turning, etc. The second part is focused on getting your robot to accomplish specific tasks as a soccer player, e.g., run toward the ball, kick the ball, block an opposing player, etc. It is probably easiest to solve these separately and link the second part as a higher-level controller that sends trajectory and goal directives to the first part.
There are a lot of relevant papers and books which could be suggested, but I've listed some potentially useful ones below that you may wish to include in whatever research you have already done.
Reading Suggestions
LaValle, Steven Michael (2006). Planning Algorithms, Cambridge University Press.
Raibert, Marc (1986). Legged Robots that Balance. MIT Press.
Vukobratovic, Miomir and Borovac, Branislav (2004). "Zero-Moment Point - Thirty Five Years of its Life", International Journal of Humanoid Robotics, Vol. 1, No. 1, pp 157–173.
Hirose, Masato and Takenaka, T (2001). "Development of the humanoid robot ASIMO", Honda R&D Technical Review, vol 13, no. 1.
Wu, QiDi and Liu, ChengJu and Zhang, JiaQi and Chen, QiJun (2009). "Survey of locomotion control of legged robots inspired by biological concept ", Science in China Series F: Information Sciences, vol 52, no. 10, pp 1715--1729, Springer.
Wahde, Mattias and Pettersson, Jimmy (2002) "A brief review of bipedal robotics research", Proceedings of the 8th Mechatronics Forum International Conference, pp 480-488.
Shan, J., Junshi, C. and Jiapin, C. (2000). "Design of central pattern generator for
humanoid robot walking based on multi-objective GA", In: Proc. of the IEEE/RSJ
International Conference on Intelligent Robots and Systems, pp. 1930–1935.
Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J., and Kanade, T. (2005). "Footstep planning for the Honda ASIMO humanoid", Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp 629-634.
I was working on a project not that dissimilar from this (making a robotic tuna) and one of the methods we were exploring was using a genetic algorithm to tune the performance of an artificial central pattern generator (in our case the pattern was a number of sine waves operating on each joint of the tail). It might be worth giving a shot, Genetic Algorithms are another one of those tools that can be incredibly powerful, if you are careful about selecting a fitness function.
Here's a great paper from 1999 by Peter Nordin and Mats G. Nordahl that outlines an evolutionary approach to controlling a humanoid robot, based on their experience building the ELVIS robot:
An Evolutionary Architecture for a Humanoid Robot
I've been thinking about this for quite some time now and I realized that you need at least two intelligent "agents" to make this work properly. The basic idea is that you have two types intelligent activity here:
Subconscious Motor Control (SMC).
Conscious Decision Making (CDM).
Training for the SMC could be done on-line... if you really think about it: defining success within motor control is basically done when you provide a signal to your robot, it evaluates that signal and either accepts it or rejects it. If your robot accepts a signal and it results in a "failure", then your robot goes "offline" and it can't accept any more signals. Defining "failure" and "offline" could be tricky, but I was thinking that it would be a failure if, for example, a sensor on the robot indicates that the robot is immobile (laying on the ground).
So your fitness function for the SMC might be something of the sort: numAcceptedSignals/numGivenSignals + numFailure
The CDM is another AI agent that generates signals and the fitness function for it could be: (numSignalsAccepted/numSignalsGenerated)/(numWinGoals/numLossGoals)
So what you do is you run the CDM and all the output that comes out of it goes to the SMC... at the end of a game you run your fitness functions. Alternately you can combine the SMC and the CDM into a single agent and you can make a composite fitness function based on the other two fitness functions. I don't know how else you could do it...
Finally, you have to determine what constitutes a learning session: is it half a game, full game, just a few moves, etc. If a game lasts 1 minute and you have a total of 8 players on the field, then the process of training could be VERY slow!
Update
Here is a quick reference to a paper that used genetic programming to create "softbots" that play soccer: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.136&rep=rep1&type=pdf
With regards to your comments: I was thinking that for the subconscious motor control (SMC), the signals would come from the conscious decision maker (CDM). This way you're evolving your SMC agent to properly handle the CDM agent's commands (signals). You want to maximize the up-time of the SMC agent regardless of what the CDM agent says.
The SMC agent receives an input, for example a vector force on a joint, and it then runs it through its processing unit to determine if it should execute that input or if it should reject it. The SMC should only execute inputs that it doesn't "think" it will recover from and it should reject inputs that it "thinks" would lead to a "catastrophic failure".
Now the SMC agent has an output: accept or reject a signal (1 or 0). The CDM can use that signal for its own training... the CDM wants to maximize the number of signals that the SMC accepts and it also wants to satisfy a goal: a high score for its own team and a low score for the opposing team. So the CDM has its own processing unit that is being evolved to satisfy both of those needs. Your reference provided a 3-layer design, while mine is only a 2-layer... I think mine was a right step in towards the 3-layer design.
One more thing to note here: is falling really a "catastrophic failure"? What if your robot falls, but the CDM makes it stand up again? I think that would be a valid behavior, so you shouldn't penalize the robot for falling... perhaps a better thing to do is penalize it for the amount of time it takes in order to perform a goal (not necessarily a soccer goal).
There is this tutorial on humanoid locomotion control that describes the software stack used on the HRP-4 humanoid (which can walk or climb stairs). It consists mainly of:
Linear inverted pendulum: a simplified model for balancing. It involves only the center of mass (COM) and ZMP already mentioned in other answers.
Trajectory optimization: the robot computes what it wants to do, ideally, for the next 2 seconds or so. It keeps recomputing this trajectory as it moves, which is known as model predictive control.
Balance control: the last stage that corrects the robot's posture based on sensor measurements and the desired trajectory.
Follow links to the academic papers and source code to learn more.