[PDF] Top 20 Reinforcement Learning for Mapping Instructions to Actions
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Reinforcement Learning for Mapping Instructions to Actions
... For this domain, we use two sets of binary fea- tures on state-action pairs (s, a). First, for each vocabulary word w, we define a feature that is one if w is the last word of a’s consumed words W 0 . These features help ... See full document
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Algorithms or Actions?:A Study in Large Scale Reinforcement Learning
... in reinforcement learning ...to actions, they did not present a detailed study on the dilemma between learning over actions or over ...and actions set sizes, and underlying ... See full document
7
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
... is learning how to execute instructions given raw visual input from relatively limited ...and actions that fail to execute ...This learning setup is inspired by work in robotics, where it is ... See full document
12
Relational Reinforcement Learning with Continuous Actions by Combining Behavioural Cloning and Locally Weighted Regression
... classical reinforcement learning framework a set of actions (A) is predefined for all of the possible states ...sible actions in S to reach a new ... See full document
11
Reinforcement Learning with Factored States and Actions
... “macro” actions learned by the PoE should not be confused with “temporally abstract ac- ...The learning and use of temporally abstract actions is an important area of current research in ... See full document
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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
... of instructions and follower ...to instructions, and merged traces created by different ...terpretable instructions and incorrect traces. To fo- cus on the learning and interpretation tasks, ... See full document
14
Learning to Teach in Cooperative Multiagent Reinforcement Learning
... inverse reinforcement learning (Ng and Russell 2000), apprenticeship learning (Abbeel and Ng 2004), and learning from demonstration (Argall et ...curriculum learning (Bengio et ... See full document
9
Determinantal Reinforcement Learning
... study reinforcement learning for controlling multiple agents in a collaborative ...those actions should also have ...forcement learning, where we learn the matrix in a way that it represents ... See full document
8
Transfer Learning for Reinforcement Learning Domains: A Survey
... agent’s learning representation by transferring a set of basis functions, Sherstov and Stone (2005) consider how to bias an agent by transferring an appropriate action ...all actions could be considered ... See full document
53
Nonparametric General Reinforcement Learning
... = learning + acting ...For learning we distinguish two (very related) aspects: (1) arriving at accurate beliefs about the future and (2) making accurate predictions about the ...future. Learning is a ... See full document
196
Reinforcement learning based dynamic band and channel selection in cognitive radio ad hoc networks
... Figures 9 and 10 show how this intentional mechan- ism is supported in the Q-table. Figure 9 shows the Q-table where the state is divided into geographic zones and time zones, and again into band groups and discrete DRE ... See full document
25
Learning to Act with RVRL Agents
... of reinforcement learning to guide action selection of cognitive agents has been shown to be a powerful technique for stochastic ...Standard Reinforcement learning techniques used to provide ... See full document
12
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... ment learning algorithm called the hierarchical reinforce- ment pricing (HRP) ...hierarchical reinforcement learning framework (Dietterich ... See full document
8
On the Sample Complexity of Reinforcement Learning
... The most common optimization method is to perform a local search to maximize T^(ëo). Unfortunately, it turns out that the exploration problem still rears its head in this computa tional problem. Let us return to example ... See full document
143
Is Epicurus the father of Reinforcement Learning?
... of Reinforcement Learning, where an agent interacts with its environment and receives feedback from ...and actions. In the Epicurean philosophy, there is a clear mapping of good as pleasure, ... See full document
5
A Review on Scope of Reinforcement Learning
... Artificial intelligence is intelligence exhibited by machines. An Intelligent machine that has been considered as a changeable ideal agent, distinguish the setting of it. It takes the actions and enlarges the ... See full document
5
Evolutionary Function Approximation for Reinforcement Learning
... dressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value ...individual actions and ... See full document
41
Composable Modular Reinforcement Learning
... inforcement learning agents with multiple simultaneous ac- tions, such as as robots with multiple effectors or a con- troller for multiple characters in a computer ...simultaneous actions (Rohanimanesh and ... See full document
8
MODIFIED ACTION VALUE METHOD APPLIED TO ‘n’-ARMED BANDIT PROBLEMS USING REINFORCEMENT LEARNING
... and Reinforcement Learning ...takes actions that maximize its chances of ...b) Actions, c) Transition probabilities, d) Transition rewards, e) a Policy and f) a Performance ... See full document
7
Learning to Interpret Natural Language Instructions
... language instructions that describe complex multipart tasks by learning from pairs of instruc- tions and behavioral traces containing a sequence of primitive actions that result in these ... See full document
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