Search Results for author:
Found 29 papers, 10 papers with code
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
Learning by Directional Gradient Descent
How should state be constructed from a sequence of observations, so as to best achieve some objective?
Emphatic Algorithms for Deep Reinforcement Learning
In this paper, we extend the use of emphatic methods to deep reinforcement learning agents.
Podracer architectures for scalable Reinforcement Learning
Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally associated with production systems. Deep learning frameworks such as TensorFlow, PyTorch and JAX allow users to transparently make use of accelerators, such as TPUs and GPUs, to offload the more computationally intensive parts of training and inference in modern deep learning systems.
Muesli: Combining Improvements in Policy Optimization
We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss.
Ranked #8 on
Atari Games
on atari game
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.
Discovering Reinforcement Learning Algorithms
Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments.
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
In this work, we propose an algorithm based on meta-gradient descent that discovers its own objective, flexibly parameterised by a deep neural network, solely from interactive experience with its environment.
Expected Eligibility Traces
The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in reinforcement learning and artificial intelligence.
A Self-Tuning Actor-Critic Algorithm
Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify hyperparameters that perform well on a new domain.
What Can Learned Intrinsic Rewards Capture?
Furthermore, we show that unlike policy transfer methods that capture "how" the agent should behave, the learned reward functions can generalise to other kinds of agents and to changes in the dynamics of the environment by capturing "what" the agent should strive to do.
Off-Policy Actor-Critic with Shared Experience Replay
We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges: (a) efficient actor-critic learning with experience replay (b) stability of off-policy learning where agents learn from other agents behaviour.
Ranked #5 on
Atari Games
on Atari-57
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions.
Behaviour Suite for Reinforcement Learning
bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.
On Inductive Biases in Deep Reinforcement Learning
These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters.
When to use parametric models in reinforcement learning?
We examine the question of when and how parametric models are most useful in reinforcement learning.
Towards Consistent Performance on Atari using Expert Demonstrations
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
In this paper we extend the SFs & GPI framework in two ways.
Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.
Deep Reinforcement Learning and the Deadly Triad
In this work, we investigate the impact of the deadly triad in practice, in the context of a family of popular deep reinforcement learning models - deep Q-networks trained with experience replay - analysing how the components of this system play a role in the emergence of the deadly triad, and in the agent's performance
Multi-task Deep Reinforcement Learning with PopArt
This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.
Ranked #1 on
Visual Navigation
on Dmlab-30
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible.
Ranked #1 on
Atari Games
on Atari 2600 Boxing
Unicorn: Continual Learning with a Universal, Off-policy Agent
Some real-world domains are best characterized as a single task, but for others this perspective is limiting.
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent improvements to the DQN algorithm.
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.
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
Dueling Network Architectures for Deep Reinforcement Learning
In recent years there have been many successes of using deep representations in reinforcement learning.
Ranked #1 on
Atari Games
on Atari 2600 Pong
