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Found 26 papers, 19 papers with code
Fast Adaptation to New Environments via Policy-Dynamics Value Functions
An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned.
Chain-of-Verification Reduces Hallucination in Large Language Models
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.
Challenges and Applications of Large Language Models
Due to the fast pace of the field, it is difficult to identify the remaining challenges and already fruitful application areas.
Improving Language Plasticity via Pretraining with Active Forgetting
Pretrained language models (PLMs) are today the primary model for natural language processing.
On the Importance of Exploration for Generalization in Reinforcement Learning
Existing approaches for improving generalization in deep reinforcement learning (RL) have mostly focused on representation learning, neglecting RL-specific aspects such as exploration.
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.
Hyperparameters in Reinforcement Learning and How To Tune Them
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting.
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents.
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
Ranked #9 on
Math Word Problem Solving
on MAWPS
Building a Subspace of Policies for Scalable Continual Learning
We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks.
Dungeons and Data: A Large-Scale NetHack Dataset
Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets.
Exploration via Elliptical Episodic Bonuses
In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
Automatic Data Augmentation for Generalization in Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments.
Open-Ended Learning Leads to Generally Capable Agents
The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.
Decoupling Value and Policy for Generalization in Reinforcement Learning
Standard deep reinforcement learning algorithms use a shared representation for the policy and value function, especially when training directly from images.
Fast Adaptation via Policy-Dynamics Value Functions
An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned.
The NetHack Learning Environment
Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack.
Ranked #1 on
NetHack Score
on NetHack Learning Environment
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Our agent outperforms other baselines specifically designed to improve generalization in RL.
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards.
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments
However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to visit a state more than once.
Backplay: 'Man muss immer umkehren'
Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency.
Backplay: "Man muss immer umkehren"
Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency.
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
