2,898 research outputs found
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.Comment: Accepted at ICML 201
PASTA: Pretrained Action-State Transformer Agents
Self-supervised learning has brought about a revolutionary paradigm shift in
various computing domains, including NLP, vision, and biology. Recent
approaches involve pre-training transformer models on vast amounts of unlabeled
data, serving as a starting point for efficiently solving downstream tasks. In
the realm of reinforcement learning, researchers have recently adapted these
approaches by developing models pre-trained on expert trajectories, enabling
them to address a wide range of tasks, from robotics to recommendation systems.
However, existing methods mostly rely on intricate pre-training objectives
tailored to specific downstream applications. This paper presents a
comprehensive investigation of models we refer to as Pretrained Action-State
Transformer Agents (PASTA). Our study uses a unified methodology and covers an
extensive set of general downstream tasks including behavioral cloning, offline
RL, sensor failure robustness, and dynamics change adaptation. Our goal is to
systematically compare various design choices and provide valuable insights to
practitioners for building robust models. Key highlights of our study include
tokenization at the action and state component level, using fundamental
pre-training objectives like next token prediction, training models across
diverse domains simultaneously, and using parameter efficient fine-tuning
(PEFT). The developed models in our study contain fewer than 10 million
parameters and the application of PEFT enables fine-tuning of fewer than 10,000
parameters during downstream adaptation, allowing a broad community to use
these models and reproduce our experiments. We hope that this study will
encourage further research into the use of transformers with first-principles
design choices to represent RL trajectories and contribute to robust policy
learning
From Rolling Over to Walking: Enabling Humanoid Robots to Develop Complex Motor Skills
This paper presents an innovative method for humanoid robots to acquire a
comprehensive set of motor skills through reinforcement learning. The approach
utilizes an achievement-triggered multi-path reward function rooted in
developmental robotics principles, facilitating the robot to learn gross motor
skills typically mastered by human infants within a single training phase. The
proposed method outperforms standard reinforcement learning techniques in
success rates and learning speed within a simulation environment. By leveraging
the principles of self-discovery and exploration integral to infant learning,
this method holds the potential to significantly advance humanoid robot motor
skill acquisition.Comment: 8 pages, 9 figures. Submitted to IEEE Robotics and Automation
Letters. Video available at https://youtu.be/d0RqrW1Ezj
- …