We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration.

本文提出了一种探索方法，将先前学习的基本技能及其动态与前瞻性搜索相结合，用于操作策略的强化学习，并且表明该方法比当前最先进的 RL 方法更快地学习到复杂操作策略，并获得更好的策略收敛结果。