3 research outputs found
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals
In a multi-agent setting, the optimal policy of a single agent is largely
dependent on the behavior of other agents. We investigate the problem of
multi-agent reinforcement learning, focusing on decentralized learning in
non-stationary domains for mobile robot navigation. We identify a cause for the
difficulty in training non-stationary policies: mutual adaptation to
sub-optimal behaviors, and we use this to motivate a curriculum-based strategy
for learning interactive policies. The curriculum has two stages. First, the
agent leverages policy gradient algorithms to learn a policy that is capable of
achieving multiple goals. Second, the agent learns a modifier policy to learn
how to interact with other agents in a multi-agent setting. We evaluated our
approach on both an autonomous driving lane-change domain and a robot
navigation domain
Experience Reuse with Probabilistic Movement Primitives
Acquiring new robot motor skills is cumbersome, as learning a skill from
scratch and without prior knowledge requires the exploration of a large space
of motor configurations. Accordingly, for learning a new task, time could be
saved by restricting the parameter search space by initializing it with the
solution of a similar task. We present a framework which is able of such
knowledge transfer from already learned movement skills to a new learning task.
The framework combines probabilistic movement primitives with descriptions of
their effects for skill representation. New skills are first initialized with
parameters inferred from related movement primitives and thereafter adapted to
the new task through relative entropy policy search. We compare two different
transfer approaches to initialize the search space distribution with data of
known skills with a similar effect. We show the different benefits of the two
knowledge transfer approaches on an object pushing task for a simulated 3-DOF
robot. We can show that the quality of the learned skills improves and the
required iterations to learn a new task can be reduced by more than 60% when
past experiences are utilized.Comment: 8 pages, 5 figures, IROS 201
A survey of benchmarking frameworks for reinforcement learning
Reinforcement learning has recently experienced increased prominence in the
machine learning community. There are many approaches to solving reinforcement
learning problems with new techniques developed constantly. When solving
problems using reinforcement learning, there are various difficult challenges
to overcome. To ensure progress in the field, benchmarks are important for
testing new algorithms and comparing with other approaches. The reproducibility
of results for fair comparison is therefore vital in ensuring that improvements
are accurately judged. This paper provides an overview of different
contributions to reinforcement learning benchmarking and discusses how they can
assist researchers to address the challenges facing reinforcement learning. The
contributions discussed are the most used and recent in the literature. The
paper discusses the contributions in terms of implementation, tasks and
provided algorithm implementations with benchmarks. The survey aims to bring
attention to the wide range of reinforcement learning benchmarking tasks
available and to encourage research to take place in a standardised manner.
Additionally, this survey acts as an overview for researchers not familiar with
the different tasks that can be used to develop and test new reinforcement
learning algorithms