Mastering diverse domains through world models
D Hafner, J Pasukonis, J Ba, T Lillicrap - ar** a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …
applications has been a fundamental challenge in artificial intelligence. Although current …
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …
[HTML][HTML] dm_control: Software and tasks for continuous control
The dm_control software package is a collection of Python libraries and task suites for
reinforcement learning agents in an articulated-body simulation. Infrastructure includes a …
reinforcement learning agents in an articulated-body simulation. Infrastructure includes a …
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …
beginning to show some successes in real-world scenarios. However, much of the research …
Multi-objective gflownets
M Jain, SC Raparthy… - International …, 2023 - proceedings.mlr.press
We study the problem of generating diverse candidates in the context of Multi-Objective
Optimization. In many applications of machine learning such as drug discovery and material …
Optimization. In many applications of machine learning such as drug discovery and material …
Pareto set learning for expensive multi-objective optimization
Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …
applications, where their objective function evaluations involve expensive computations or …
Scalar reward is not enough: A response to silver, singh, precup and sutton (2021)
The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
An empirical investigation of the challenges of real-world reinforcement learning
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …
beginning to show some successes in real-world scenarios. However, much of the research …