2,430 research outputs found
Reinforcement learning for robot control using probability density estimations
Presentado al ICINCO 2010 celebrado en Funchal (Portugal) del 15 al 18 de junio.The successful application of Reinforcement Learning (RL) techniques to robot control is limited by the fact that, in most robotic tasks, the state and action spaces are continuous, multidimensional, and in essence, too large for conventional RL algorithms to work. The well known curse of dimensionality makes infeasible using a tabular representation of the value function, which is the classical approach that provides convergence guarantees. When a function approximation technique is used to generalize among similar states, the convergence of the algorithm is compromised, since updates unavoidably affect an extended region of the domain, that is, some situations are modified in a way that has not been really experienced, and the update may degrade the approximation. We propose a RL algorithm that uses a probability density estimation in the joint space of states, actions and Q-values as a means of function approximation. This allows us to devise an updating approach that, taking into account the local sampling density, avoids an excessive modification of the approximation far from the observed sample.This work was supported by the project 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069). This research was partially supported by Consolider Ingenio 2010, project CSD2007-00018.Peer Reviewe
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
A major difficulty of solving continuous POMDPs is to infer the multi-modal
distribution of the unobserved true states and to make the planning algorithm
dependent on the perceived uncertainty. We cast POMDP filtering and planning
problems as two closely related Sequential Monte Carlo (SMC) processes, one
over the real states and the other over the future optimal trajectories, and
combine the merits of these two parts in a new model named the DualSMC network.
In particular, we first introduce an adversarial particle filter that leverages
the adversarial relationship between its internal components. Based on the
filtering results, we then propose a planning algorithm that extends the
previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with
an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex
observations such as image input but also it remains highly interpretable. It
is shown to be effective in three continuous POMDP domains: the floor
positioning domain, the 3D light-dark navigation domain, and a modified Reacher
domain.Comment: IJCAI 202
Generative Exploration and Exploitation
Sparse reward is one of the biggest challenges in reinforcement learning
(RL). In this paper, we propose a novel method called Generative Exploration
and Exploitation (GENE) to overcome sparse reward. GENE automatically generates
start states to encourage the agent to explore the environment and to exploit
received reward signals. GENE can adaptively tradeoff between exploration and
exploitation according to the varying distributions of states experienced by
the agent as the learning progresses. GENE relies on no prior knowledge about
the environment and can be combined with any RL algorithm, no matter on-policy
or off-policy, single-agent or multi-agent. Empirically, we demonstrate that
GENE significantly outperforms existing methods in three tasks with only binary
rewards, including Maze, Maze Ant, and Cooperative Navigation. Ablation studies
verify the emergence of progressive exploration and automatic reversing.Comment: AAAI'2
Task Transfer by Preference-Based Cost Learning
The goal of task transfer in reinforcement learning is migrating the action
policy of an agent to the target task from the source task. Given their
successes on robotic action planning, current methods mostly rely on two
requirements: exactly-relevant expert demonstrations or the explicitly-coded
cost function on target task, both of which, however, are inconvenient to
obtain in practice. In this paper, we relax these two strong conditions by
developing a novel task transfer framework where the expert preference is
applied as a guidance. In particular, we alternate the following two steps:
Firstly, letting experts apply pre-defined preference rules to select related
expert demonstrates for the target task. Secondly, based on the selection
result, we learn the target cost function and trajectory distribution
simultaneously via enhanced Adversarial MaxEnt IRL and generate more
trajectories by the learned target distribution for the next preference
selection. The theoretical analysis on the distribution learning and
convergence of the proposed algorithm are provided. Extensive simulations on
several benchmarks have been conducted for further verifying the effectiveness
of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed
equally to this wor
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