26,847 research outputs found
Grounding Language for Transfer in Deep Reinforcement Learning
In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.Comment: JAIR 201
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
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