Eligibility Traces for Actor-Critic methods
Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay
... Sampling Actor-Critic (NISAC), a set of empirically validated modifications to the advantage actor-critic algorithm (A2C), allowing off-policy reinforcement learning and increased ...on-policy ...
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Natural Actor-Critic Algorithms
... policy-gradient methods known as actor–critic methods. These methods can be thought of as reinforcement learning analogs of dynamic programming’s policy iteration ...method. ...
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Towards Feature Selection In Actor-Critic Algorithms
... the critic in actor-critic algorithms with function approximation is known to be a ...few critic features can lead to degeneracy of the actor gradient, and too many features may lead to ...
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Bayesian Policy Gradient and Actor-Critic Algorithms
... In this paper, we first proposed an alternative approach to the conventional frequentist (Monte-Carlo based) policy gradient estimation procedure. Our approach is based on Bayesian quadrature (O’Hagan, 1991), a Bayesian ...
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Least squares temporal difference actor-critic methods with applications to robot motion control
... Abstract— We consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by ...
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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
... optimization problems. Consequently, game-theoretic formalisms are often used as the basis for representing interactions and decision-making in multiagent systems [17, 79, 64]. Computer poker is a common multiagent ...
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Actor-Critic Fictitious Play in Simultaneous Move Multistage Games
... ceive a reward informing on how good was their ac- tion when performed in the state they were in. The goal of MARL is to learn a strategy that maximally accumulates rewards over time. Whilst the problem is fairly well ...
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A Convergent Online Single Time Scale Actor Critic Algorithm
... In general, policy selection may be randomized. When facing problems with a large number of states or actions (or even continuous state-action problems), effective policy selection may suffer from several problems, such ...
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Recursive Least-Squares Learning with Eligibility Traces
... The rest of the paper is organized as follows. Sec. 2 introduces the background of Markov Decision Processes and describes the state-of-the-art algorithms for on-policy learning with recursive LS methods. Sec. 3 ...
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Off-policy Learning With Eligibility Traces: A Survey
... The rest of this article is organized as follows. Section 2 introduces the background of Markov Decision Processes, describes the state-of-the-art algorithms for learning without eligibility traces, and ...
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Sample efficient Actor Critic Reinforcement Learning with Supervised Data for Dialogue Management
... 2. efficient utilisation of demonstration data for improved early stage policy learning. The first part focusses primarily on increasing the RL learning speed. For TRACER, trust regions are introduced to standard ...
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Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot
... d Department of Electrical Engineering, Yuan Ze University, Taiwan Abstract As a combination of robotic motion planning and Chinese calligraphy cul- ture, robotic calligraphy plays a significant role in the inheritance ...
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Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience
... an actor-critic al- gorithm that uses extra data from past planning experience in addition to reward ...standard actor-critic al- gorithm (Konda and Tsitsiklis 2003), a value function is first ...
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Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
... Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing Zheqi Zhu, Shuo Wan, Pingyi Fan, Senior Member, IEEE, Khaled ...multi-agent actor-critic (H-MAAC), is proposed as a ...
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Adaptive proportional fair parameterization based LTE scheduling using continuous actor critic reinforcement learning
... the eligibility of CACLA-2 actor-critic RL algorithm in comparison with other methods, the considered scenario fluctuates at each 1s the number of active users based on the -greedy ...
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End-to-end Reinforcement Learning for Autonomous Longitudinal Control Using Advantage Actor Critic with Temporal Context
... The critic network allows the algorithm to estimate the value of the actions taken during ...gradient methods where total rewards are calculated at the end of the episode so that network weights can be ...
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Adaptive dynamic programming with eligibility traces and complexity reduction of high-dimensional systems
... traditional methods, our approach engages all properties of the original system by using agglomerative hierarchical clustering of system poles depending on a performance ...
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Using Sliding Mode Controller and Eligibility Traces for Controlling the Blood Glucose in Diabetic Patients at the Presence of Fault
... mentioned methods to remove the chattering in simulation ...the eligibility traces algorithm in combination with the sliding mode control can control the blood glucose and insulin levels with a high ...
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Replicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methods
... Abstract. Reinforcement Learning (RL) is a subfield of Artificial Intelligence (AI) that deals with agents navigating in an environment with the goal of maximizing total reward. Games are good environments to test RL ...
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An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention.
... of actor-critic al- gorithm and the bootstrap confidence intervals proposed in the previous sections under a variety of generative ...bandit actor critic algorithm deteriorates when the amount ...
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