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Deploying our policy learned inside of the omdels RNN environment back into the actual VizDoom environment. However, the more general Learning To Think approach is not limited to this rather naive approach. In this section, we World models rc how we can train the Agent model described earlier to solve a car racing task. To train our V model, Women forcing men to wear panties first collect a dataset of 10, random rollouts of the environment. Other World models rc works use Bayesian neural networks instead of GPs to learn a dynamics model. By training together with an M that predicts rewards, the VAE may learn to focus on task-relevant areas of the image, but the tradeoff here is that we may not be able to reuse the VAE effectively for new tasks without retraining. Subsequent works have used RNN-based models to generate many frames into the futureand also as an internal modeos to World models rc about the future. In the figure below, we plot the results of same agent evaluated over rollouts:. Instead it allows a recurrent C to learn to address "subroutines" of the recurrent M, and reuse them for problem solving in arbitrary computable ways, e. DoomRNN is more computationally efficient compared to VizDoom as it only operates in latent space without the modfls to render an image at each time step, and we do not need to run the actual Doom game engine. We learn to perceive time spatially when we read comics. To optimize the parameters of C, we chose the Covariance-Matrix Adaptation Evolution Strategy CMA-ES as our optimization algorithm since it is known to work well for solution spaces of up to a few thousand parameters. Like a seasoned Formula One driver or the baseball player discussed earlier, the agent can instinctively predict when and where to navigate in the heat of the moment.
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- We explore building generative neural network models of popular reinforcement learning environments.
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For instance, if the agent selects the left action, the M model learns to move the agent to the left and adjust its internal representation of the game states accordingly. To train our V model, we first collect a dataset of 10, random rollouts of the environment. That's what Old School Model Works is all about. As a result of using M to generate a virtual environment for our agent, we are also giving the controller access to all of the hidden states of M. An exciting research direction is to look at ways to incorporate artificial curiosity and intrinsic motivation and information seeking abilities in an agent to encourage novel exploration. The driving is more stable, and the agent is able to seemingly attack the sharp corners effectively. This input is usually a 2D image frame that is part of a video sequence.
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By increasing the uncertainty, our dream environment becomes more difficult compared to the actual environment. The fireballs may move more randomly in a less predictable path compared to the actual game. Sometimes the agent may even die due to sheer misfortune, without explanation. We find agents that perform well in higher temperature settings generally perform better in the normal setting.
We took the agent trained inside of the virtual environment and tested its performance on the original VizDoom scenario. We will discuss how this score compares to other models later on. We see that even though the V model is not able to capture all of the details of each frame correctly, for instance, getting the number of monsters correct, the agent is still able to use the learned policy to navigate in the real environment.
As the virtual environment cannot even keep track of the exact number of monsters in the first place, an agent that is able to survive the noisier and uncertain virtual nightmare environment will thrive in the original, cleaner environment. In our childhood, we may have encountered ways to exploit video games in ways that were not intended by the original game designer.
Players discover ways to collect unlimited lives or health, and by taking advantage of these exploits, they can easily complete an otherwise difficult game. However, in the process of doing so, they may have forfeited the opportunity to learn the skill required to master the game as intended by the game designer.
In our initial experiments, we noticed that our agent discovered an adversarial policy to move around in such a way so that the monsters in this virtual environment governed by M never shoots a single fireball during some rollouts. Even when there are signs of a fireball forming, the agent moves in a way to extinguish the fireballs. Because M is only an approximate probabilistic model of the environment, it will occasionally generate trajectories that do not follow the laws governing the actual environment.
As we previously pointed out, even the number of monsters on the other side of the room in the actual environment is not exactly reproduced by M. For this reason, our world model will be exploitable by C, even if such exploits do not exist in the actual environment. As a result of using M to generate a virtual environment for our agent, we are also giving the controller access to all of the hidden states of M. This is essentially granting our agent access to all of the internal states and memory of the game engine, rather than only the game observations that the player gets to see.
Therefore our agent can efficiently explore ways to directly manipulate the hidden states of the game engine in its quest to maximize its expected cumulative reward. The weakness of this approach of learning a policy inside of a learned dynamics model is that our agent can easily find an adversarial policy that can fool our dynamics model -- it will find a policy that looks good under our dynamics model, but will fail in the actual environment, usually because it visits states where the model is wrong because they are away from the training distribution.
This weakness could be the reason that many previous works that learn dynamics models of RL environments do not actually use those models to fully replace the actual environments. Like in the M model proposed in , the dynamics model is deterministic, making it easily exploitable by the agent if it is not perfect.
Using Bayesian models, as in PILCO , helps to address this issue with the uncertainty estimates to some extent, however, they do not fully solve the problem. Recent work combines the model-based approach with traditional model-free RL training by first initializing the policy network with the learned policy, but must subsequently rely on model-free methods to fine-tune this policy in the actual environment.
To make it more difficult for our C to exploit deficiencies of M, we chose to use the MDN-RNN as the dynamics model of the distribution of possible outcomes in the actual environment, rather than merely predicting a deterministic future. Even if the actual environment is deterministic, the MDN-RNN would in effect approximate it as a stochastic environment.
Using a mixture of Gaussian model may seem excessive given that the latent space encoded with the VAE model is just a single diagonal Gaussian distribution. However, the discrete modes in a mixture density model are useful for environments with random discrete events, such as whether a monster decides to shoot a fireball or stay put. While a single diagonal Gaussian might be sufficient to encode individual frames, an RNN with a mixture density output layer makes it easier to model the logic behind a more complicated environment with discrete random states.
M is not able to transition to another mode in the mixture of Gaussian model where fireballs are formed and shot. Whatever policy learned inside of this generated environment will achieve a perfect score of most of the time, but will obviously fail when unleashed into the harsh reality of the actual world, underperforming even a random policy.
The temperature also affects the types of strategies the agent discovers. In our experiments, the tasks are relatively simple, so a reasonable world model can be trained using a dataset collected from a random policy. But what if our environments become more sophisticated?
In any difficult environment, only parts of the world are made available to the agent only after it learns how to strategically navigate through its world.
For more complicated tasks, an iterative training procedure is required. We need our agent to be able to explore its world, and constantly collect new observations so that its world model can be improved and refined over time. An iterative training procedure, adapted from Learning To Think is as follows:. We have shown that one iteration of this training loop was enough to solve simple tasks.
For more difficult tasks, we need our controller in Step 2 to actively explore parts of the environment that is beneficial to improve its world model. An exciting research direction is to look at ways to incorporate artificial curiosity and intrinsic motivation and information seeking abilities in an agent to encourage novel exploration.
In particular, we can augment the reward function based on improvement in compression quality. In the present approach, since M is a MDN-RNN that models a probability distribution for the next frame, if it does a poor job, then it means the agent has encountered parts of the world that it is not familiar with.
Therefore we can adapt and reuse M's training loss function to encourage curiosity. By flipping the sign of M's loss function in the actual environment, the agent will be encouraged to explore parts of the world that it is not familiar with.
The new data it collects may improve the world model. The iterative training procedure requires the M model to not only predict the next observation x x x and d o n e done d o n e , but also predict the action and reward for the next time step. This may be required for more difficult tasks. For instance, if our agent needs to learn complex motor skills to walk around its environment, the world model will learn to imitate its own C model that has already learned to walk. After difficult motor skills, such as walking, is absorbed into a large world model with lots of capacity, the smaller C model can rely on the motor skills already absorbed by the world model and focus on learning more higher level skills to navigate itself using the motor skills it had already learned.
Another related connection is to muscle memory. For instance, as you learn to do something like play the piano, you no longer have to spend working memory capacity on translating individual notes to finger motions -- this all becomes encoded at a subconscious level. An interesting connection to the neuroscience literature is the work on hippocampal replay that examines how the brain replays recent experiences when an animal rests or sleeps. Replaying recent experiences plays an important role in memory consolidation -- where hippocampus-dependent memories become independent of the hippocampus over a period of time.
As Foster puts it, replay is "less like dreaming and more like thought". We invite readers to read Replay Comes of Age for a detailed overview of replay from a neuroscience perspective with connections to theoretical reinforcement learning. Iterative training could allow the C--M model to develop a natural hierarchical way to learn. Recent works about self-play in RL and PowerPlay also explores methods that lead to a natural curriculum learning , and we feel this is one of the more exciting research areas of reinforcement learning.
There is extensive literature on learning a dynamics model, and using this model to train a policy. Many concepts first explored in the s for feed-forward neural networks FNNs and in the s for RNNs laid some of the groundwork for Learning to Think. The more recent PILCO is a probabilistic model-based search policy method designed to solve difficult control problems.
Using data collected from the environment, PILCO uses a Gaussian process GP model to learn the system dynamics, and then uses this model to sample many trajectories in order to train a controller to perform a desired task, such as swinging up a pendulum, or riding a unicycle.
While Gaussian processes work well with a small set of low dimensional data, their computational complexity makes them difficult to scale up to model a large history of high dimensional observations. Other recent works use Bayesian neural networks instead of GPs to learn a dynamics model. These methods have demonstrated promising results on challenging control tasks , where the states are known and well defined, and the observation is relatively low dimensional.
Here we are interested in modelling dynamics observed from high dimensional visual data where our input is a sequence of raw pixel frames. In robotic control applications, the ability to learn the dynamics of a system from observing only camera-based video inputs is a challenging but important problem. Early work on RL for active vision trained an FNN to take the current image frame of a video sequence to predict the next frame , and use this predictive model to train a fovea-shifting control network trying to find targets in a visual scene.
To get around the difficulty of training a dynamical model to learn directly from high-dimensional pixel images, researchers explored using neural networks to first learn a compressed representation of the video frames.
Recent work along these lines was able to train controllers using the bottleneck hidden layer of an autoencoder as low-dimensional feature vectors to control a pendulum from pixel inputs.
Learning a model of the dynamics from a compressed latent space enable RL algorithms to be much more data-efficient. Video game environments are also popular in model-based RL research as a testbed for new ideas. Guzdial et al. Learning to predict how different actions affect future states in the environment is useful for game-play agents, since if our agent can predict what happens in the future given its current state and action, it can simply select the best action that suits its goal.
This has been demonstrated not only in early work when compute was a million times more expensive than today but also in recent studies on several competitive VizDoom environments. The works mentioned above use FNNs to predict the next video frame.
We may want to use models that can capture longer term time dependencies. RNNs are powerful models suitable for sequence modelling. He trained RNNs to learn the structure of such a game and then showed that they can hallucinate similar game levels on its own. Using RNNs to develop internal models to reason about the future has been explored as early as in a paper called Making the World Differentiable , and then further explored in.
A more recent paper called Learning to Think presented a unifying framework for building a RNN-based general problem solver that can learn a world model of its environment and also learn to reason about the future using this model.
Subsequent works have used RNN-based models to generate many frames into the future , and also as an internal model to reason about the future. In this work, we used evolution strategies ES to train our controller, as it offers many benefits. For instance, we only need to provide the optimizer with the final cumulative reward, rather than the entire history. ES is also easy to parallelize -- we can launch many instances of rollout with different solutions to many workers and quickly compute a set of cumulative rewards in parallel.
Recent works have confirmed that ES is a viable alternative to traditional Deep RL methods on many strong baseline tasks. Before the popularity of Deep RL methods , evolution-based algorithms have been shown to be effective at finding solutions for RL tasks. Evolution-based algorithms have even been able to solve difficult RL tasks from high dimensional pixel inputs. We have demonstrated the possibility of training an agent to perform tasks entirely inside of its simulated latent space world.
This approach offers many practical benefits. For instance, video game engines typically require heavy compute resources for rendering the game states into image frames, or calculating physics not immediately relevant to the game.
We may not want to waste cycles training an agent in the actual environment, but instead train the agent as many times as we want inside its simulated environment. Agents that are trained incrementally to simulate reality may prove to be useful for transferring policies back to the real world.
Our approach may complement sim2real approaches outlined in previous work. Furthermore, we can take advantage of deep learning frameworks to accelerate our world model simulations using GPUs in a distributed environment. The benefit of implementing the world model as a fully differentiable recurrent computation graph also means that we may be able to train our agents in the dream directly using the backpropagation algorithm to fine-tune its policy to maximize an objective function.
The choice of implementing V as a VAE and training it as a standalone model also has its limitations, since it may encode parts of the observations that are not relevant to a task. After all, unsupervised learning cannot, by definition, know what will be useful for the task at hand. For instance, our VAE reproduced unimportant detailed brick tile patterns on the side walls in the Doom environment, but failed to reproduce task-relevant tiles on the road in the Car Racing environment.
By training together with an M that predicts rewards, the VAE may learn to focus on task-relevant areas of the image, but the tradeoff here is that we may not be able to reuse the VAE effectively for new tasks without retraining. Learning task-relevant features has connections to neuroscience as well. Primary sensory neurons are released from inhibition when rewards are received, which suggests that they generally learn task-relevant features, rather than just any features, at least in adulthood.
Another concern is the limited capacity of our world model. While modern storage devices can store large amounts of historical data generated using an iterative training procedure, our LSTM-based world model may not be able to store all of the recorded information inside of its weight connections.
While the human brain can hold decades and even centuries of memories to some resolution , our neural networks trained with backpropagation have more limited capacity and suffer from issues such as catastrophic forgetting. Future work will explore replacing the VAE and MDN-RNN with higher capacity models , or incorporating an external memory module , if we want our agent to learn to explore more complicated worlds.
Like early RNN-based C--M systems , ours simulates possible futures time step by time step, without profiting from human-like hierarchical planning or abstract reasoning, which often ignores irrelevant spatial-temporal details. However, the more general Learning To Think approach is not limited to this rather naive approach.
Instead it allows a recurrent C to learn to address "subroutines" of the recurrent M, and reuse them for problem solving in arbitrary computable ways, e. A recent One Big Net extension of the C--M approach collapses C and M into a single network, and uses PowerPlay-like behavioural replay where the behaviour of a teacher net is compressed into a student net to avoid forgetting old prediction and control skills when learning new ones.
Experiments with those more general approaches are left for future work. If you would like to discuss any issues or give feedback, please visit the GitHub repository of this page for more information. The interative demos in this article were all built using p5.
Deploying all of these machine learning models in a web browser was made possible with deeplearn. A special thanks goes to Nikhil Thorat and Daniel Smilkov for their support. We would like to thank Chris Olah and the rest of the Distill editorial team for their valuable feedback and generous editorial support, in addition to supporting the use of their distill.
Any errors here are our own and do not reflect opinions of our proofreaders and colleagues. If you see mistakes or want to suggest changes, feel free to contribute feedback by participating in the discussion forum for this article. The instructions to reproduce the experiments in this work is available here. In this section we will describe in more details the models and training methods used in this work.
In the following diagram, we describe the shape of our tensor at each layer of the ConvVAE and also describe the details of each layer:. As the environment may give us observations as high dimensional pixel images, we first resize each image to 64x64 pixels and use this resized image as V's observation. Each pixel is stored as three floating point values between 0 and 1 to represent each of the RGB channels. Each convolution and deconvolution layer uses a stride of 2.
All convolutional and deconvolutional layers use relu activations except for the output layer as we need the output to be between 0 and 1. We use this network to model the probability distribution of the next z z z in the next time step as a Mixture of Gaussian distribution.
The only difference in the approach used is that we did not model the correlation parameter between each element of z z z , and instead had the MDN-RNN output a diagonal covariance matrix of a factored Gaussian distribution. This approach is very similar to previous work in the Unconditional Handwriting Generation section and also the decoder-only section of SketchRNN. We would sample from this pdf at each time step to generate the environments.
Given that death is a low probability event at each time step, we find the cutoff approach to be more stable compared to sampling from the Bernoulli distribution. For instance, in the Car Racing task, the steering wheel has a range from In the Doom environment, we converted the discrete actions into a continuous action space between Following the approach described in Evolving Stable Strategies , we used a population size of 64, and had each agent perform the task 16 times with different initial random seeds.
The agent's fitness value is the average cumulative reward of the 16 random rollouts. The figure below charts the best performer, worst performer, and mean fitness of the population of 64 agents at each generation:. Since the requirement of this environment is to have an agent achieve an average score above over random rollouts, we took the best performing agent at the end of every 25 generations, and tested it over random rollout scenarios to record this average on the red line.
After generations, an agent was able to achieve an average score of We used random rollouts rather than because each process of the 64 core machine had been configured to run 16 times already, effectively using a full generation of compute after every 25 generations to evaluate the best agent times. In the figure below, we plot the results of same agent evaluated over rollouts:.
These results are shown in the two figures below:. Please note that we did not attempt to train our agent on the actual VizDoom environment, but only used VizDoom for the purpose of collecting training data using a random policy.
DoomRNN is more computationally efficient compared to VizDoom as it only operates in latent space without the need to render an image at each time step, and we do not need to run the actual Doom game engine.
The best agent managed to obtain an average score of over random rollouts.
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