1,107 research outputs found
Lifeworld Analysis
We argue that the analysis of agent/environment interactions should be
extended to include the conventions and invariants maintained by agents
throughout their activity. We refer to this thicker notion of environment as a
lifeworld and present a partial set of formal tools for describing structures
of lifeworlds and the ways in which they computationally simplify activity. As
one specific example, we apply the tools to the analysis of the Toast system
and show how versions of the system with very different control structures in
fact implement a common control structure together with different conventions
for encoding task state in the positions or states of objects in the
environment.Comment: See http://www.jair.org/ for any accompanying file
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with increasing task horizon or action dimensionality. This puts many
real-world tasks out of practical reach of RL methods. In this work, we use
demonstrations to overcome the exploration problem and successfully learn to
perform long-horizon, multi-step robotics tasks with continuous control such as
stacking blocks with a robot arm. Our method, which builds on top of Deep
Deterministic Policy Gradients and Hindsight Experience Replay, provides an
order of magnitude of speedup over RL on simulated robotics tasks. It is simple
to implement and makes only the additional assumption that we can collect a
small set of demonstrations. Furthermore, our method is able to solve tasks not
solvable by either RL or behavior cloning alone, and often ends up
outperforming the demonstrator policy.Comment: 8 pages, ICRA 201
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