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Found 17 papers, 8 papers with code
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
The Phenomenon of Policy Churn
We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning.
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.
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
To test our hypothesis empirically, we augmented a standard deep RL agent with an auxiliary task of learning the value-improvement path.
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.
Recurrent Experience Replay in Distributed Reinforcement Learning
Using a single network architecture and fixed set of hyperparameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and surpasses the state of the art on DMLab-30.
Ranked #1 on
Atari Games
on Atari 2600 Video Pinball
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
In this paper we extend the SFs & GPI framework in two ways.
Universal Successor Features Approximators
We focus on one aspect in particular, namely the ability to generalise to unseen tasks.
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.
StarCraft II: A New Challenge for Reinforcement Learning
Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.
Ranked #1 on
Starcraft II
on MoveToBeacon
Distral: Robust Multitask Reinforcement Learning
Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
Ranked #1 on
class-incremental learning
on cifar100
Prioritized Experience Replay
Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.
Ranked #3 on
Atari Games
on Atari 2600 Kangaroo
