Search Results for author:
Found 14 papers, 9 papers with code
Hierarchical Reinforcement Learning in Complex 3D Environments
Hierarchical Reinforcement Learning (HRL) agents have the potential to demonstrate appealing capabilities such as planning and exploration with abstraction, transfer, and skill reuse.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings.
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
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery
Reinforcement learning (RL) agents performing complex tasks must be able to remember observations and actions across sizable time intervals.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
Grounded Language Learning in a Simulated 3D World
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
Learning to reinforcement learn
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
Learning to Navigate in Complex Environments
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.
Progressive Neural Networks
Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence.
Leveraging Monolingual Data for Crosslingual Compositional Word Representations
In this work, we present a novel neural network based architecture for inducing compositional crosslingual word representations.
