AIDE: AI-Driven Exploration in the Space of Code

1 code implementation • 18 Feb 2025 • Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth, Dixing Xu, Ian Kaplan, Deniss Jacenko, Yuxiang Wu

Machine learning, the foundation of modern artificial intelligence, has driven innovations that have fundamentally transformed the world.

H-GAP: Humanoid Control with a Generalist Planner

no code implementations • 5 Dec 2023 • Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian

However, the extensive collection of human motion-captured data and the derived datasets of humanoid trajectories, such as MoCapAct, paves the way to tackle these challenges.

Humanoid Control Model Predictive Control +1

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

1 code implementation • 6 Jun 2023 • Linjie Xu, Zhengyao Jiang, Jinyu Wang, Lei Song, Jiang Bian

Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time.

D4RL MuJoCo +4

Optimal Transport for Offline Imitation Learning

1 code implementation • 24 Mar 2023 • Yicheng Luo, Zhengyao Jiang, samuel cohen, Edward Grefenstette, Marc Peter Deisenroth

In this paper, we introduce Optimal Transport Reward labeling (OTR), an algorithm that assigns rewards to offline trajectories, with a few high-quality demonstrations.

D4RL Imitation Learning +2

Efficient Planning in a Compact Latent Action Space

1 code implementation • 22 Aug 2022 • Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian

Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces.

continuous-control Continuous Control +3

Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

1 code implementation • 31 May 2022 • Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette

In this paper, we treat the transition data of the MDP as a graph, and define a novel backup operator, Graph Backup, which exploits this graph structure for better value estimation.

Atari Games counterfactual +3

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

1 code implementation • 8 Feb 2021 • Zhengyao Jiang, Pasquale Minervini, Minqi Jiang, Tim Rocktaschel

In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents.

reinforcement-learning Reinforcement Learning (RL)

Neural Logic Reinforcement Learning

1 code implementation • 24 Apr 2019 • Zhengyao Jiang, Shan Luo

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks.

Deep Reinforcement Learning Inductive logic programming +3

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

30 code implementations • 30 Jun 2017 • Zhengyao Jiang, Dixing Xu, Jinjun Liang

They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market.

Deep Reinforcement Learning Management +3

Cryptocurrency Portfolio Management with Deep Reinforcement Learning

3 code implementations • 5 Dec 2016 • Zhengyao Jiang, Jinjun Liang

Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products.

Decision Making Deep Reinforcement Learning +3