source site To call a taxi, I recommend downloading the Ola app and using it. Note: I do not recommend Uber in India.
I had the worst experience I have ever had while travelling in India when taking an Uber to the airport in Delhi recently. Autorickshaws can be a great way to go shorter distances. Make sure you have the fare fixed before you start. There is usually room for some negotiation. In touristy areas, they tend to inflate the fares for foreigners, sometimes even doubling them. And in really crowded places like Old Delhi, Varanasi, or the Haridwar bazaar, the cycle rickshaw is the way to go.
Personally, I tend not to negotiate with these guys because they work so hard for their money. These are the most common ways to travel in India, but there are many other options. If you ride a motorcycle or scooter, you can rent them, or join motorcycle trips. In Rajasthan, you can go on camel or horse safaris. In Kerala, you can travel by houseboat on the Backwaters.
In national parks and tiger reserves, you can travel by an open, jeep-type vehicle, the Maruti Gypsy. Some people may want to ride an elephant in India, but I strongly advise against it. I arrived at the New Delhi Train Station in the muggy pre-dawn hours, amid the usual chaos of honking autorickshaws and hordes of people. A pack of red-turbaned porters stood at the ready as the fat ambassador taxis disgorged their passengers. I steeled myself for the usual assault. And sure enough, before my foot hit the broken, moist pavement, three of them were on me.
And yes, as I was barely awake, it was a relief to have someone carry my bag through the teeming railway station to the platform that was almost a kilometre away. I followed my porter, who was of course running ahead, thinking it was my lucky day: he was very tall, which made him easier to spot as he raced through the crowd with my luggage on his head.
We arrived at the platform and I showed him my ticket. After some confusion, and consultation with a notice board that listed all the passengers, he pointed out that I had a waiting list ticket only. Number 48 on the waiting list. I thought 48 was my seat number. I really needed to get to my yoga ashram.
The Kumbh Mela was only days away and millions of people would be streaming to my destination. There would be no chance to get another train. He grasped the situation immediately, and sprang into action, sprinting up and down the platform looking for a conductor. We found the first class conductor surrounded by questioning passengers. A chubby, satisfied-looking babu in a worn uniform, he said it was impossible. All the trains to Haridwar were booked for weeks. On to the train we jumped, together, united in our sense of urgency and exhilarated by our success.
The train was packed, but the porter found a place overhead to squeeze in my bag as the final boarding call resounded up and down the damp, cavernous platform. The porter and I looked at each other and smiled, accomplices now, and I gave him a heart-felt thank you as I thrust a small handful of rupee notes in his hand, much more than he tried to scam off me. Professional travel writer Mariellen Ward is the founder of award-winning Breathedreamgo. Mariellen has a BA in Journalism and has been travel writing and blogging since Table of Contents.
Although in principle, we can train V and M together in an end-to-end manner, we found that training each separately is more practical, achieves satisfactory results, and does not require exhaustive hyperparameter tuning. As images are not required to train M on its own, we can even train on large batches of long sequences of latent vectors encoding the entire frames of an episode to capture longer term dependencies, on a single GPU.
In this experiment, the world model V and M has no knowledge about the actual reward signals from the environment. Its task is simply to compress and predict the sequence of image frames observed. Only the Controller C Model has access to the reward information from the environment. Since there are a mere parameters inside the linear controller model, evolutionary algorithms such as CMA-ES are well suited for this optimization task.
The figure below compares actual the observation given to the agent and the observation captured by the world model. Training an agent to drive is not a difficult task if we have a good representation of the observation. Previous works have shown that with a good set of hand-engineered information about the observation, such as LIDAR information, angles, positions and velocities, one can easily train a small feed-forward network to take this hand-engineered input and output a satisfactory navigation policy.
Although the agent is still able to navigate the race track in this setting, we notice it wobbles around and misses the tracks on sharper corners. The driving is more stable, and the agent is able to seemingly attack the sharp corners effectively. Furthermore, we see that in making these fast reflexive driving decisions during a car race, the agent does not need to plan ahead and roll out hypothetical scenarios of the future.
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. Traditional Deep RL methods often require pre-processing of each frame, such as employing edge-detection , in addition to stacking a few recent frames into the input. In contrast, our world model takes in a stream of raw RGB pixel images and directly learns a spatial-temporal representation.
To our knowledge, our method is the first reported solution to solve this task. Since our world model is able to model the future, we are also able to have it come up with hypothetical car racing scenarios on its own. We can put our trained C back into this dream environment generated by M. The following demo shows how our world model can be used to generate the car racing environment:. We have just seen that a policy learned inside of the real environment appears to somewhat function inside of the dream environment. This begs the question -- can we train our agent to learn inside of its own dream, and transfer this policy back to the actual environment?
If our world model is sufficiently accurate for its purpose, and complete enough for the problem at hand, we should be able to substitute the actual environment with this world model. After all, our agent does not directly observe the reality, but only sees what the world model lets it see. In this experiment, we train an agent inside the dream environment generated by its world model trained to mimic a VizDoom environment. The agent must learn to avoid fireballs shot by monsters from the other side of the room with the sole intent of killing the agent.
There are no explicit rewards in this environment, so to mimic natural selection, the cumulative reward can be defined to be the number of time steps the agent manages to stay alive during a rollout. The setup of our VizDoom experiment is largely the same as the Car Racing task, except for a few key differences. Since the M model can predict the d o n e done d o n e state in addition to the next observation, we now have all of the ingredients needed to make a full RL environment.
We first build an OpenAI Gym environment interface by wrapping a gym. Env interface over our M if it were a real Gym environment, and then train our agent inside of this virtual environment instead of using the actual environment. In this simulation, we don't need the V model to encode any real pixel frames during the hallucination process, so our agent will therefore only train entirely in a latent space environment. This has many advantages that will be discussed later on.
This virtual environment has an identical interface to the real environment, so after the agent learns a satisfactory policy in the virtual environment, we can easily deploy this policy back into the actual environment to see how well the policy transfers over. After some training, our controller learns to navigate around the dream environment and escape from deadly fireballs launched by monsters generated by the M model.
The following demo shows how our agent navigates inside its own dream. The M model learns to generate monsters that shoot fireballs at the direction of the agent, while the C model discovers a policy to avoid these generated fireballs. Here, our RNN-based world model is trained to mimic a complete game environment designed by human programmers. By learning only from raw image data collected from random episodes, it learns how to simulate the essential aspects of the game -- such as the game logic, enemy behaviour, physics, and also the 3D graphics rendering.
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. It also learns to block the agent from moving beyond the walls on both sides of the level if the agent attempts to move too far in either direction. Occasionally, the M model needs to keep track of multiple fireballs being shot from several different monsters and coherently move them along in their intended directions.
It must also detect whether the agent has been killed by one of these fireballs. Unlike the actual game environment, however, we note that it is possible to add extra uncertainty into the virtual environment, thus making the game more challenging in the dream environment. 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.
Our Lady of Guadalupe holds a special place in the religious life of Mexico and is one of the most popular religious devotions. Her image has played an important role as a national symbol of Mexico. Climb to the top of the Pyramid of the sun and feel the mysticism of one of the most incredible places in the world. When you arrive at Mr.
Sancho's Beach Club on Cozumel, your all-inclusive day pass allows you to enjoy its many amenities. Relax along the longest beachfront location on the island, home to 1, feet meters of clean white sand, and go swimming in the clear Caribbean water. Fill up on unlimited food from the buffet and drinks at the pool bar, and take a nap in a hammock, if the mood strikes. It's up to you how you spend your time at the club.
The reason your goal is not apparent is that you need to make the decision. Stored value cards are also available for Children, Students and Elders, all of which DO offer a variety of discounts:. Cons: Agonizingly slow along most downtown routes. Learn how your comment data is processed. To be bumped on a flight indicates that you are feeling left out in social gatherings, the actual "bumping" of the flight is a sign of the feelings of rejection in waking life. Superstitions Dictionary Popular superstitions uncovered.
Please note: This pass does not include entrance to the aquatic park. You will be met at the airport and be taken directly to your hotel in the Cancun area. An English-speaking representative will greet you when you clear airport customs and your vacation can start! North America. Welcome to Mexico. An Outdoor Life With steaming jungles, snowcapped volcanoes, cactus-strewn deserts and 10,km of coast strung with sandy beaches and wildlife-rich lagoons, Mexico is an endless adventure for the senses and a place where life is lived largely in the open air. A Varied Palate Mexico's gastronomic repertoire is as diverse as the country's people and topography.
Los Mexicanos At the heart of your Mexican experience will be the Mexican people. Top experiences in Mexico. Launch map view. Recent articles. Mexico activities. Cozumel Mr.