153,411 research outputs found
Multi-Agent Behavior-Based Policy Transfer
A key objective of transfer learning is to improve and speedup learning on a target task after training on a different, but related, source task. This study presents a neuro-evolution method that transfers evolved policies within multi-agent tasks of varying degrees of complexity. The method incorporates behavioral diversity (novelty) search as a means to boost the task performance of transferred policies (multi-agent behaviors). Results indicate that transferred evolved multi-agent behaviors are significantly improved in more complex tasks when adapted using behavioral diversity. Comparatively, behaviors that do not use behavioral diversity to further adapt transferred behaviors, perform relatively poorly in terms of adaptation times and quality of solutions in target tasks. Also, in support of previous work, both policy transfer methods (with and without behavioral diversity adaptation), out-perform behaviors evolved in target tasks without transfer learning
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
Generating Long-term Trajectories Using Deep Hierarchical Networks
We study the problem of modeling spatiotemporal trajectories over long time
horizons using expert demonstrations. For instance, in sports, agents often
choose action sequences with long-term goals in mind, such as achieving a
certain strategic position. Conventional policy learning approaches, such as
those based on Markov decision processes, generally fail at learning cohesive
long-term behavior in such high-dimensional state spaces, and are only
effective when myopic modeling lead to the desired behavior. The key difficulty
is that conventional approaches are "shallow" models that only learn a single
state-action policy. We instead propose a hierarchical policy class that
automatically reasons about both long-term and short-term goals, which we
instantiate as a hierarchical neural network. We showcase our approach in a
case study on learning to imitate demonstrated basketball trajectories, and
show that it generates significantly more realistic trajectories compared to
non-hierarchical baselines as judged by professional sports analysts.Comment: Published in NIPS 201
- …