Search Results for author:
Found 10 papers, 6 papers with code
Leading the Pack: N-player Opponent Shaping
We evaluate on over 4 different environments, varying the number of players from 3 to 5, and demonstrate that model-based OS methods converge to equilibrium with better global welfare than naive learning.
Generalization to New Sequential Decision Making Tasks with In-Context Learning
By training on large diverse offline datasets, our model is able to learn new MiniHack and Procgen tasks without any weight updates from just a handful of demonstrations.
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy.
Understanding the Effects of RLHF on LLM Generalisation and Diversity
OOD generalisation is crucial given the wide range of real-world scenarios in which these models are being used, while output diversity refers to the model's ability to generate varied outputs and is important for a variety of use cases.
Reward Model Ensembles Help Mitigate Overoptimization
Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger "gold" reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used.
Domain Generalization for Robust Model-Based Offline Reinforcement Learning
Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin.
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions
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.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
This survey is an overview of this nascent field.
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.
