3,315 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
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation
Robotic systems are ever more capable of automation and fulfilment of complex
tasks, particularly with reliance on recent advances in intelligent systems,
deep learning and artificial intelligence. However, as robots and humans come
closer in their interactions, the matter of interpretability, or explainability
of robot decision-making processes for the human grows in importance. A
successful interaction and collaboration will only take place through mutual
understanding of underlying representations of the environment and the task at
hand. This is currently a challenge in deep learning systems. We present a
hierarchical deep reinforcement learning system, consisting of a low-level
agent handling the large actions/states space of a robotic system efficiently,
by following the directives of a high-level agent which is learning the
high-level dynamics of the environment and task. This high-level agent forms a
representation of the world and task at hand that is interpretable for a human
operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based
model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its
performance. Results show efficient learning of complex actions/states spaces
by the low-level agent, and an interpretable representation of the task and
decision-making process learned by the high-level agent
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