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Found 26 papers, 13 papers with code
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model.
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
In this work, we aim to bring the best of both worlds and propose an algorithm that exhibits an exploratory behavior whilst it utilizes large diverse datasets.
Entropic Desired Dynamics for Intrinsic Control
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
Wasserstein Distance Maximizing Intrinsic Control
This paper deals with the problem of learning a skill-conditioned policy that acts meaningfully in the absence of a reward signal.
Discovering Diverse Nearly Optimal Policies with Successor Features
We propose Diverse Successive Policies, a method for discovering policies that are diverse in the space of Successor Features, while assuring that they are near optimal.
Relative Variational Intrinsic Control
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment.
Q-Learning in enormous action spaces via amortized approximate maximization
Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions.
Unsupervised Learning of Object Keypoints for Perception and Control
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
Fast Task Inference with Variational Intrinsic Successor Features
It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from other policies \citep{gregor2016variational, eysenbach2018diversity, warde2018unsupervised}.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL).
The Uncertainty Bellman Equation and Exploration
In this paper we consider a similar \textit{uncertainty} Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps.
Reinforcement Learning with Unsupervised Auxiliary Tasks
We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task.
Combining policy gradient and Q-learning
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting.
Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.
Using Fast Weights to Attend to the Recent Past
Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs.
Strategic Attentive Writer for Learning Macro-Actions
We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting.
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function that is being approximated.
Ranked #12 on
Atari Games
on Atari 2600 Centipede
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
Ranked #9 on
Atari Games
on Atari 2600 Star Gunner
Policy Distillation
Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance.
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinforcement learning.
Ranked #17 on
Atari Games
on Atari 2600 Private Eye
Human level control through deep reinforcement learning
We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.
Multiple Object Recognition with Visual Attention
We present an attention-based model for recognizing multiple objects in images.
Recurrent Models of Visual Attention
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Generating more realistic images using gated MRF's
Probabilistic models of natural images are usually evaluated by measuring performance on rather indirect tasks, such as denoising and inpainting.
