RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.

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To get started, take a look over the custom env example and the API documentation. If you’re looking to develop custom algorithms with RLlib, also check out concepts and custom algorithms.

RLlib in 60 seconds¶

Ray-Ban offers the best quality sunglasses with a classic style. Available in a variety of shapes and colors, Ray-Ban sunglasses are retro style made modern. With polarized lenses and UV protection for your eyes, these sunglasses are built to keep up with you as you move through your day. The year 2021 marks the centenary of Satyajit Ray, and Netflix’s new anthology Ray is a tribute to the maestro. Based on short stories written by Ray, the anthology is a collection of four films.

The following is a whirlwind overview of RLlib. For a more in-depth guide, see also the full table of contents and RLlib blog posts. You may also want to skim the list of built-in algorithms. Look out for the and icons to see which algorithms are available for each framework.

Running RLlib¶

RLlib has extra dependencies on top of ray. First, you’ll need to install either PyTorch or TensorFlow. Then, install the RLlib module:

Then, you can try out training in the following equivalent ways:

Next, we’ll cover three key concepts in RLlib: Policies, Samples, and Trainers.

Policies¶

Policies are a core concept in RLlib. In a nutshell, policies are Python classes that define how an agent acts in an environment. Rollout workers query the policy to determine agent actions. In a gym environment, there is a single agent and policy. In vector envs, policy inference is for multiple agents at once, and in multi-agent, there may be multiple policies, each controlling one or more agents:

Policies can be implemented using any framework. However, for TensorFlow and PyTorch, RLlib has build_tf_policy and build_torch_policy helper functions that let you define a trainable policy with a functional-style API, for example:

Sample Batches¶

Whether running in a single process or large cluster, all data interchange in RLlib is in the form of sample batches. Sample batches encode one or more fragments of a trajectory. Typically, RLlib collects batches of size rollout_fragment_length from rollout workers, and concatenates one or more of these batches into a batch of size train_batch_size that is the input to SGD.

A typical sample batch looks something like the following when summarized. Since all values are kept in arrays, this allows for efficient encoding and transmission across the network:

In multi-agent mode, sample batches are collected separately for each individual policy.

Training¶

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Policies each define a learn_on_batch() method that improves the policy given a sample batch of input. For TF and Torch policies, this is implemented using a loss function that takes as input sample batch tensors and outputs a scalar loss. Here are a few example loss functions:

Simple policy gradient loss
Simple Q-function loss
Importance-weighted APPO surrogate loss

RLlib Trainer classes coordinate the distributed workflow of running rollouts and optimizing policies. They do this by leveraging Ray parallel iterators to implement the desired computation pattern. The following figure shows synchronous sampling, the simplest of these patterns:

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RLlib uses Ray actors to scale training from a single core to many thousands of cores in a cluster. You can configure the parallelism used for training by changing the num_workers Serial for office 2011 mac. parameter. Check out our scaling guide for more details here.

Application Support¶

Beyond environments defined in Python, RLlib supports batch training on offline datasets, and also provides a variety of integration strategies for external applications. Slender man for mac.

Customization¶

RLlib provides ways to customize almost all aspects of training, includingneural network models,action distributions,policy definitions:the environment,and the sample collection process

To learn more, proceed to the table of contents.

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If he had already been blind, he could not have blamed himself for the death and would not have carried the lifelong guilt that, the movie argues, contributed to his eventual drug addiction. Would he also then have not been driven to become the consummate artist that he was? Who can say? For that matter, what role did blindness play in his genius? Did it make him so alive to sound that he became a better musician? Certainly he was so attuned to the world around him that he never used a cane or a dog; for Charles blindness was more of an attribute than a handicap.

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Jamie Foxx suggests the complexities of Ray Charles in a great, exuberant performance. He doesn't do the singing -- that's all Ray Charles on the soundtrack -- but what would be the point? Ray Charles was deeply involved in the project for years, until his death in June, and the film had access to his recordings, so of course it should use them, because nobody else could sing like Ray Charles.

What Foxx gets just right is the physical Ray Charles, and what an extrovert he was. Not for Ray the hesitant blind man of cliche feeling his way, afraid of the wrong step. In the movie and in life, he was adamantly present in body as well as spirit, filling a room, physically dominant, interlaced with other people. Yes, he was eccentric in his mannerisms, especially at the keyboard; I can imagine a performance in which Ray Charles would come across like a manic clown. But Foxx correctly interprets the musician's body language as a kind of choreography, in which he was conducting his music with himself, instead of with a baton. Foxx so accurately reflects my own images and memories of Charles that I abandoned thoughts of how much 'like' Charles he was and just accepted him as Charles, and got on with the story.

The movie places Charles at the center of key movements in postwar music. After an early career in which he seemed to aspire to sound like Nat 'King' Cole, he loosened up, found himself, and discovered a fusion between the gospel music of his childhood and the rhythm & blues of his teen years and his first professional gigs. The result was, essentially, the invention of soul music, in early songs like 'I Got a Woman.'

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The movie shows him finding that sound in Seattle, his improbable destination after he leaves his native Georgia. Before and later, it returns for key scenes involving his mother Aretha (Sharon Warren), who taught him not to be intimidated by his blindness, to dream big, to demand the best for himself. She had no education and little money, but insisted that he attend the school for the blind, which set him on his way. He heads for Seattle after hearing about the club scene, but why there and not in New York, Kansas City, Chicago or New Orleans? Certainly his meeting with the Seattle teenager Quincy Jones was one of the crucial events in his life (as was his friendship with the dwarf emcee Oberon, played by Warwick Davis, who turns him on to pot).

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