Working at Rainbow Design Services: Employee Reviews | blogmaths.info
Dec 9, If you're a fan of musicals, there's a good chance you've heard your fair know someone who you meet online, introduce to your family and marry? The former Reading Rainbow host shares short stories in his .. has a particularly great interview with former gymnast and human meme McKayla Maroney. Taking a camping vacation to Rainbow Beach! Pinki Cola calls her Pinki Cola is one of the bubbliest Lil' Shoppies you'll ever meet. You'll never feel flat when. 'It is a good reward but he must maintain his calm and. Man United posters on my walls ever since I was little and having the chance to meet.
Super Lines have a 5: Productions have an 8: Extended performance time is available up to 1: Dancers are expected to perform in the order published, unless granted special permission by the Competition Director or pre-approved by the Rainbow Customer Care Specialists prior to the competition. Dances not performing within 10 acts after their scheduled performance time may not be eligible for Overall High Point awards, unless pre-approved by the Rainbow Customer Care Specialists or by the Competition Director.
Incomplete acts may result in point deductions or be scored lower by the judging panel. If the original performance is not completed, the act will receive an automatic High Gold award and is ineligible for High Point awards. Contestants in the first 10 scheduled acts should arrive ready to perform in full costume, hair and make-up. All other competing dancers must be ready to perform at least one hour prior to their scheduled performance time.
For safety purposes, all props freestanding or hand-held are restricted to a maximum height of 15 feet.
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The use of safety railings is recommended. Hanging backdrops and special lighting may not be used. Helium balloons are only allowed if they are weighted and if permitted by the contracted venue. There is no guarantee that power outlets will be available for props that require electricity. Battery-operated props are recommended. If the weight of a prop exceeds the maximum weight limit for a stage, or if the Competition Director feels the weight of the prop is a safety hazard, the dancers will not be allowed to use the prop.
When sliding props or moving scenery, do not alter or damage the backdrop, wings or marley dance floor. Props must be loaded in and out of the venue on the same day they are used.
Storing props in the wings or backstage is not allowed. Rainbow will not be responsible for props left overnight or unattended. Any special requests for the assembling of props must be discussed with the Competition Director upon arrival to the venue. All props MUST be labeled with the studio name for identification purposes.
Toy weapons are acceptable. However, toys may not fire projectile objects or have blunt points or sharp edges. Hoverboards and motor vehicles are not allowed. All props should be non-toxic substances. No form of liquid, gel, powder, glass objects, aerosol cans of any type including hairspray or spray paint or similar substances may be used that will physically alter the backdrop, wings or surface of the marley floor.
Please contact the Rainbow office if you need further clarification on our prop and set rules. Special note regarding rosin: Rosin must be self-contained and cannot be applied to shoes directly on the marley floor. If a performance is requesting to use rosin, Rainbow must know upon registration so that schedule adjustments can be made to clean up the marley residue post-performance.
There may be point deductions if Rainbow is not notified about the rosin usage and the marley floor is affected during the act. Lines are expected to set-up props in 1: Super Lines are expected to set-up props in 2: Productions are expected to set-up props in 2: Contestants must also agree that the time, manner, and method of judging the competition is at the discretion of Rainbow Dance Competition.
In the event that an infraction was overlooked during a Regional competition, it will be taken into account at National Finals. Technique — 40 points total 2. Stage Presence — 25 points total 3. Execution of Performance — 20 points total 4. Choreography — 10 points total 5. Technique — 30 points total 2. Overall Entertainment Value — 25 points total 3. Stage Presence — 20 points total 4. Execution of Choreography — 15 points total 5.
Costume — 10 points total There is a maximum of points per judge for a total of points. The total points attainable varies per level. If a dance, costume, or music is deemed inappropriate for family viewing by the judging panel, it will be scored lower by judges and may not be eligible for Overall High Point awards.
Ties will not be broken for the general competition awards. However, all ties will be broken for Overall High Point awards. Dancer of the Year scores will be determined at the same time as the Solo performance. There will be two separate and unique score sheets; one for the Solo performance and one for the Dancer of the Year performance.
General awards are based on composite scores and not on placement within an age group in each category. This may result in multiple Double Platinum, Platinum and High Gold awards presented in each age group for each category.
Each act will receive one plaque per performance and individual award pins for the dancers in the act. Although acts are competing against an adjudicated point system for General awards, all contestants will compete against each other for the Overall High Point awards within their level and age division. If there is only one act competing in a particular division, that act will be judged against a point system. The point break for all given awards is determined by the scoring range of that particular competition.
Any act that performs outside of its original scheduled performance time, will receive General award placements at its assigned awards ceremony. Cash awards will always be given to the overall winner regardless of the number of entries in each division. All acts must perform on the same day and before their awards ceremony to be eligible for Overall High Point Awards.
If an act does not compete on the scheduled day and time, only a General award will be presented unless pre-approved by the Customer Care Specialists or Competition Director. Entries received after the program is printed are also not eligible for High Point Awards unless pre-approved by the Customer Care Specialists or Competition Director. Should an act drop below the minimum number requirement for the group size after the program is printed and Rainbow is notified before the start of the competition, the act may compete in the appropriate division and time for eligibility.
Any entry that does not comply may be disqualified. The final policy learned to be suicidal, because negative reward was plentiful, positive reward was too hard to achieve, and a quick death ending in 0 reward was preferable to a long life that risked negative reward.
A friend is training a simulated robot arm to reach towards a point above a table. The policy learned to slam the table really hard, making the table fall over, which moved the target point too. The target point just so happened to fall next to the end of the arm. A researcher gives a talk about using RL to train a simulated robot hand to pick up a hammer and hammer in a nail.
Initially, the reward was defined by how far the nail was pushed into the hole. Instead of picking up the hammer, the robot used its own limbs to punch the nail in.
So, they added a reward term to encourage picking up the hammer, and retrained the policy. They got the policy to pick up the hammer…but then it threw the hammer at the nail instead of actually using it. However, none of it sounds implausible to me. I know people who like to tell stories about paperclip optimizers. I get it, I really do. To me, this implies a clever, out-of-the-box solution that gives more reward than the intended answer of the reward function designer.
Reward hacking is the exception. The much more common case is a poor local optima that comes from getting the exploration-exploitation trade-off wrong. This is an implementation of Normalized Advantage Functionlearning on the HalfCheetah environment.
From an outside perspective, this is really, really dumb. In random exploration, the policy found falling forward was better than standing still. It explored the backflip enough to become confident this was a good idea, and now backflipping is burned into the policy.
Once the policy is backflipping consistently, which is easier for the policy: I would guess the latter. In this run, the initial random weights tended to output highly positive or highly negative action outputs. This makes most of the actions output the maximum or minimum acceleration possible. These are both cases of the classic exploration-exploitation problem that has dogged reinforcement learning since time immemorial.
Your data comes from your current policy. If your current policy explores too much you get junk data and learn nothing. There are several intuitively pleasing ideas for addressing this - intrinsic motivation, curiosity-driven exploration, count-based exploration, and so forth.
Many of these approaches were first proposed in the s or earlier, and several of them have been revisited with deep learning models. However, as far as I know, none of them work consistently across all environments.
To quote WikipediaOriginally considered by Allied scientists in World War II, it proved so intractable that, according to Peter Whittle, the problem was proposed to be dropped over Germany so that German scientists could also waste their time on it. DQN can solve a lot of the Atari games, but it does so by focusing all of learning on a single goal - getting really good at one game. To forestall some obvious comments: In some cases, you get such a distribution for free.
An example is navigation, where you can sample goal locations randomly, and use universal value functions to generalize. I find this work very promising, and I give more examples of this work later. OpenAI Universe tried to spark this, but from what I heard, it was too difficult to solve, so not much got done. Raghu et al, By training player 2 against the optimal player 1, we showed RL could reach high performance. Lanctot et al, NIPS showed a similar result.
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Here, there are two agents playing laser tag. The agents are trained with multiagent reinforcement learning. To test generalization, they run the training with 5 random seeds. As you can see, they learn to move towards and shoot each other.
Deep Reinforcement Learning Doesn't Work Yet
Then, they took player 1 from one experiment, and pitted it against player 2 from a different experiment. If the learned policies generalize, we should see similar behavior. This seems to be a running theme in multiagent RL.
When agents are trained against one another, a kind of co-evolution happens. The agents get really good at beating each other, but when they get deployed against an unseen player, performance drops.
Same learning algorithm, same hyperparameters. The diverging behavior is purely from randomness in initial conditions. That being said, there are some neat results from competitive self-play environments that seem to contradict this.
OpenAI has a nice blog post of some of their work in this space. Self-play is also an important part of both AlphaGo and AlphaZero. As you relax from symmetric self-play to general multiagent settings, it gets harder to ensure learning happens at the same speed.
Often, these are picked by hand, or by random search. Supervised learning is stable. Fixed dataset, ground truth targets. Not all hyperparameters perform well, but with all the empirical tricks discovered over the years, many hyperparams will show signs of life during training. When I started working at Google Brain, one of the first things I did was implement the algorithm from the Normalized Advantage Function paper.
I figured it would only take me about weeks. I had several things going for me: It ended up taking me 6 weeks to reproduce results, thanks to several software bugs. The question is, why did it take so long to find these bugs? The input state is 3-dimensional. The action space is 1-dimensional, the amount of torque to apply. The goal is to balance the pendulum perfectly straight up. Reward is defined by the angle of the pendulum. Actions bringing the pendulum closer to the vertical not only give reward, they give increasing reward.
The reward landscape is basically concave. Below is a video of a policy that mostly works. Here is a plot of performance, after I fixed all the bugs.
Each line is the reward curve from one of 10 independent runs. Same hyperparameters, the only difference is the random seed. Seven of these runs worked. The environment is HalfCheetah. The y-axis is episode reward, the x-axis is number of timesteps, and the algorithm used is TRPO. The dark line is the median performance over 10 random seeds, and the shaded region is the 25th to 75th percentile. But on the other hand, the 25th percentile line is really close to 0 reward.
The core thesis is that machine learning adds more dimensions to your space of failure cases, which exponentially increases the number of ways you can fail. Deep RL adds a new dimension: And the only way you can address random chance is by throwing enough experiments at the problem to drown out the noise.
When your training algorithm is both sample inefficient and unstable, it heavily slows down your rate of productive research. Maybe it only takes 1 million steps. But when you multiply that by 5 random seeds, and then multiply that with hyperparam tuning, you need an exploding amount of compute to test hypotheses effectively. Your ResNets, batchnorms, or very deep networks have no power here.
RL must be forced to work. If pure randomness is enough to lead to this much variance between runs, imagine how much an actual difference in the code could make.
Among its conclusions are: Multiplying the reward by a constant can cause significant differences in performance. Five random seeds a common reporting metric may not be enough to argue significant results, since with careful selection you can get non-overlapping confidence intervals.
Different implementations of the same algorithm have different performance on the same task, even when the same hyperparameters are used. My theory is that RL is very sensitive to both your initialization and to the dynamics of your training process, because your data is always collected online and the only supervision you get is a single scalar for reward.
A policy that fails to discover good training examples in time will collapse towards learning nothing at all, as it becomes more confident that any deviation it tries will fail. Deep reinforcement learning has certainly done some very cool things. DQN is old news now, but was absolutely nuts at the time. A single model was able to learn directly from raw pixels, without tuning for each game individually. And AlphaGo and AlphaZero continue to be very impressive achievements.
I tried to think of real-world, productionized uses of deep RL, and it was surprisingly difficult. I expected to find something in recommendation systems, but I believe those are still dominated by collaborative filtering and contextual bandits. In the end, the best I could find were two Google projects: Jack Clark from OpenAI tweeted a similar request and found a similar conclusion.
The tweet is from last year, before AutoML was announced. Salesforce has their text summarization model, which worked if you massaged the RL carefully enough.
Of course, finance companies have reasons to be cagey about how they play the market, so perhaps the evidence there is never going to be strong. I think the former is more likely. I have trouble seeing the same happen with deep RL.
That being said, we can draw conclusions from the current list of deep reinforcement learning successes. These are projects where deep RL either learns some qualitatively impressive behavior, or it learns something better than comparable prior work. Admittedly, this is a very subjective criteria. Things mentioned in the previous sections: Firoiu et al, They use counterfactual regret minimization and clever iterative solving of subgames. From this list, we can identify common properties that make learning easier.
None of the properties below are required for learning, but satisfying more of them is definitively better. It is easy to generate near unbounded amounts of experience. It should be clear why this helps. The more data you have, the easier the learning problem is. This applies to Atari, Go, Chess, Shogi, and the simulated environments for the parkour bot.
It likely applies to the power center project too, because in prior work Gao,it was shown that neural nets can predict energy efficiency with high accuracy. It might apply to the Dota 2 and SSBM work, but it depends on the throughput of how quickly the games can be run, and how many machines were available to run them.
The problem is simplified into an easier form. Reinforcement learning can do anything! The OpenAI Dota 2 bot only played the early game, only played Shadow Fiend against Shadow Fiend in a 1v1 laning setting, used hardcoded item builds, and presumably called the Dota 2 API to avoid having to solve perception.
The SSBM bot acheived superhuman performance, but it was only in 1v1 games, with Captain Falcon only, on Battlefield only, in an infinite time match.
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The broad trend of all research is to demonstrate the smallest proof-of-concept first and generalize it later. There is a way to introduce self-play into learning.
I should note that by self-play, I mean exactly the setting where the game is competitive, and both players can be controlled by the same agent. So far, that setting seems to have the most stable and well-performing behavior.
Two player games have this: Any time you introduce reward shaping, you introduce a chance for learning a non-optimal policy that optimizes the wrong objective. See this Terrence Tao blog post for an approachable example. As for learnability, I have no advice besides trying it out to see if it works. If the reward has to be shaped, it should at least be rich. In Dota 2, reward can come from last hits triggers after every monster kill by either playerand health triggers after every attack or skill that hits a target.
These reward signals come quick and often.
For the SSBM bot, reward can be given for damage dealt and taken, which gives signal for every attack that successfully lands.
The shorter the delay between action and consequence, the faster the feedback loop gets closed, and the easier it is for reinforcement learning to figure out a path to high reward. Neural Architecture Search We can combine a few of the principles to analyze the success of Neural Architecture Search.
According to the initial ICLR versionafter examples, deep RL was able to design state-of-the art neural net architectures.