Groot: Learning to follow instructions by watching gameplay videos
We study the problem of building a controller that can follow open-ended instructions in
open-world environments. We propose to follow reference videos as instructions, which offer …
open-world environments. We propose to follow reference videos as instructions, which offer …
Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions
Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …
particularly in generating texts, images, and videos using models trained from offline data …
Unleashing the power of pre-trained language models for offline reinforcement learning
Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected
datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline …
datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline …
Reinformer: Max-return sequence modeling for offline rl
As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as
sequence modeling that conditions on the hindsight information including returns, goal or …
sequence modeling that conditions on the hindsight information including returns, goal or …
Pre-training goal-based models for sample-efficient reinforcement learning
Pre-training on task-agnostic large datasets is a promising approach for enhancing the
sample efficiency of reinforcement learning (RL) in solving complex tasks. We present …
sample efficiency of reinforcement learning (RL) in solving complex tasks. We present …
Weighting online decision transformer with episodic memory for offline-to-online reinforcement learning
Offline reinforcement learning (RL) has been shown to be successfully modeled as a
sequence modeling problem, drawing inspiration from the success of Transformers. Offline …
sequence modeling problem, drawing inspiration from the success of Transformers. Offline …
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
Decision-making is a dynamic process requiring perception, memory, and reasoning to
make choices and find optimal policies. Traditional approaches to decision-making suffer …
make choices and find optimal policies. Traditional approaches to decision-making suffer …
Hindsight Preference Learning for Offline Preference-based Reinforcement Learning
Offline preference-based reinforcement learning (RL), which focuses on optimizing policies
using human preferences between pairs of trajectory segments selected from an offline …
using human preferences between pairs of trajectory segments selected from an offline …
Enhancing Cross-domain Pre-Trained Decision Transformers with Adaptive Attention
Recently, the pre-training of decision transformers (DT) using a different domain, such as
natural language text, has generated significant attention in offline reinforcement learning …
natural language text, has generated significant attention in offline reinforcement learning …
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
A longstanding goal of artificial general intelligence is highly capable generalists that can
learn from diverse experiences and generalize to unseen tasks. The language and vision …
learn from diverse experiences and generalize to unseen tasks. The language and vision …