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Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective
Trust Region Policy Optimization
TLDR
A method for optimizing control policies, with guaranteed monotonic improvement, by making several approximations to the theoretically-justified scheme, called Trust Region Policy Optimization (TRPO).
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
TLDR
Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods.
High-Dimensional Continuous Control Using Generalized Advantage Estimation
TLDR
This work addresses the large number of samples typically required and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias.
OpenAI Gym
TLDR
This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.
On First-Order Meta-Learning Algorithms
TLDR
A family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates, including Reptile, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task.
Theano: A Python framework for fast computation of mathematical expressions
TLDR
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Benchmarking Deep Reinforcement Learning for Continuous Control
TLDR
This work presents a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, task with partial observations, and tasks with hierarchical structure.
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
TLDR
This paper proposes to represent a "fast" reinforcement learning algorithm as a recurrent neural network (RNN) and learn it from data, encoded in the weights of the RNN, which are learned slowly through a general-purpose ("slow") RL algorithm.
Variational Lossy Autoencoder
TLDR
This paper presents a simple but principled method to learn global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN with greatly improve generative modeling performance of VAEs.
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