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Found 16 papers, 6 papers with code
Learning to Achieve Goals with Belief State Transformers
We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the prefix and the previous token for the suffix.
Video Occupancy Models
We introduce a new family of video prediction models designed to support downstream control tasks.
Unified Auto-Encoding with Masked Diffusion
At the core of both successful generative and self-supervised representation learning models there is a reconstruction objective that incorporates some form of image corruption.
Reward Centering
We show that discounted methods for solving continuing reinforcement learning problems can perform significantly better if they center their rewards by subtracting out the rewards' empirical average.
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Our system, called V-PTR, combines the benefits of pre-training on video data with robotic offline RL approaches that train on diverse robot data, resulting in value functions and policies for manipulation tasks that perform better, act robustly, and generalize broadly.
Ignorance is Bliss: Robust Control via Information Gating
We propose \textit{information gating} as a way to learn parsimonious representations that identify the minimal information required for a task.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications.
Learning Representations for Pixel-based Control: What Matters and Why?
A wide range of methods have been proposed to enable efficient learning, leading to sample complexities similar to those in the full state setting.
Learning Minimal Representations with Model Invariance
The common representation acts as a implicit invariance objective to avoid the different spurious correlations captured by individual predictors.
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Unlike previous forms of state abstractions, a model-invariance state abstraction leverages causal sparsity over state variables.
Mirror Descent Policy Optimization
Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks.
Multi-step Greedy Reinforcement Learning Algorithms
We derive model-free RL algorithms based on $\kappa$-PI and $\kappa$-VI in which the surrogate problem can be solved by any discrete or continuous action RL method, such as DQN and TRPO.
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning
In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration.
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion.
Successor Options: An Option Discovery Framework for Reinforcement Learning
This work adopts a complementary approach, where we attempt to discover options that navigate to landmark states.
