One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, these dynamics are described by physics-based analytical models, which may however be very hard to find for complex problems. More recently, we have seen learning approaches that can predict the effect of more complex physical interactions directly from sensory input. However, it is an open question how far these models generalize beyond their training data. In this work, we analyse how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a neural network to convert the raw sensory data into a suitable representation that can be consumed by the analytical model and compare this approach to using neural networks for both, perception and prediction. Our results show that the combined method outperforms the purely learned version in terms of accuracy and generalization to push actions not seen during training. It also performs comparable to the analytical model applied on ground truth input values, despite using raw sensory data as input.
Many recent approaches in reinforcement learning aim to solve the so called pixels to torque'' problem, where the network processes images to extract a representation of the state and then directly returns the required action to achieve a certain task @cite_6 @cite_12 . argue that the state-representation learned by such methods can be improved by enforcing on the extracted state, that may include e.g. temporal coherence. This is an alternative way of including basic principles of physics in a learning approach, compared to what we propose here. While policy learning requires understanding the effect of actions, the above methods do not acquire an explicit dynamics model. We are interested in learning such an explicit model, as it enables optimal action selection (potentially over a larger time horizon). The following papers share this aim.
@cite_12 @cite_6
1594297126 2950462959 2897697565 1957496711
Reinforcement learning is a type of unsupervised learning for sequential decision making. Q-learning is probably the best-understood reinforcement learning algorithm. In Q-learning, the agent learns a mapping from states and actions to their utilities. An important assumption of Q-learning is the Markovian environment assumption, meaning that any information needed to determine the optimal actions is reflected in the agent''s state representation. Consider an agent whose state representation is based solely on its immediate perceptual sensations. When its sensors are not able to make essential distinctions among world states, the Markov assumption is violated, causing a problem called perceptual aliasing. For example, when facing a closed box, an agent based on its current visual sensation cannot act optimally if the optimal action depends on the contents of the box. There are two basic approaches to addressing this problem -- using more sensors or using history to figure out the current world state. This paper studies three connectionist approaches which learn to use history to handle perceptual aliasing: the window-Q, recurrent-Q, and recurrent-model architectures. Empirical study of these architectures is presented. Their relative strengths and weaknesses are also discussed. Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space. We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain. Deep reinforcement learning has achieved great progress recently in domains such as learning to play Atari games from raw pixel input. The model-free characteristics of reinforcement learning free us from hand-encoding complex policies. However, for real world tasks such as autonomous driving, there are some complex sequential decision making processes that contain distinct behaviors. Due to the delayed rewards and the averaged gradient, it is pretty difficult for a flat deep reinforcement learning algorithm to learn a good policy. In this paper, we design a hierarchical neural network policy and propose a hierarchical policy gradient method to train the network with the semi markov decision process (SMDP) temporal abstraction formulation. We apply this method to a traffic light passing scenario in autonomous driving, where the vehicle has two distinct behaviors (e.g., pass and stop) and its primitive actions (e.g., acceleration) should follow the corresponding behavior. We show via simulation that our method is able to select correct decision and acts appropriately when the traffic light turns yellow. On the contrary, the flat reinforcement learning algorithm is not able to achieve a good performance and exhibits a large variance. Furthermore, the trained neural network modules are reusable in the future to cover more scenarios. Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model that uses deep auto-encoders to learn a low-dimensional embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning ensures that not only static but also dynamic properties of the data are accounted for. This is crucial for long-term predictions, which lie at the core of the adaptive model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art reinforcement learning methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces and is an important step toward fully autonomous learning from pixels to torques.