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The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.

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Submitted 12 April, ; v1 submitted 16 June, ; originally announced June Authors: Johannes Heinrich , David Silver. Abstract : Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge.

Our method combines fictitious self-play with deep reinforcement learning. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on significant domain expertise. Submitted 28 June, ; v1 submitted 3 March, ; originally announced March Comments: updated version, incorporating conference feedback.

Abstract : Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior.

Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance. Submitted 16 August, ; v1 submitted 24 February, ; originally announced February This version includes the appendix. Abstract : We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

Submitted 16 June, ; v1 submitted 4 February, ; originally announced February Journal ref: ICML Abstract : Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control -- deterministic policy gradient and stochastic value gradient -- to solve partially observed domains using recurrent neural networks trained with backpropagation through time. We demonstrate that this approach, coupled with long-short term memory is able to solve a variety of physical control problems exhibiting an assortment of memory requirements.

These include the short-term integration of information from noisy sensors and the identification of system parameters, as well as long-term memory problems that require preserving information over many time steps. We also demonstrate success on a combined exploration and memory problem in the form of a simplified version of the well-known Morris water maze task. Finally, we show that our approach can deal with high-dimensional observations by learning directly from pixels.

We find that recurrent deterministic and stochastic policies are able to learn similarly good solutions to these tasks, including the water maze where the agent must learn effective search strategies. Submitted 14 December, ; originally announced December Abstract : Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.

In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently.

We use prioritized experience replay in Deep Q-Networks DQN , a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41 out of 49 games.

Submitted 25 February, ; v1 submitted 18 November, ; originally announced November Abstract : We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the Bellman equation as a deterministic function of exogenous noise.

The product is a spectrum of general policy gradient algorithms that range from model-free methods with value functions to model-based methods without value functions.

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We use learned models but only require observations from the environment in- stead of observations from model-predicted trajectories, minimizing the impact of compounded model errors. We apply these algorithms first to a toy stochastic control problem and then to several physics-based control problems in simulation.

One of these variants, SVG 1 , shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains. Submitted 30 October, ; originally announced October Comments: 13 pages, NIPS Abstract : The popular Q-learning algorithm is known to overestimate action values under certain conditions.

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It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari domain.

We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games. Submitted 8 December, ; v1 submitted 22 September, ; originally announced September Comments: AAAI Authors: Timothy P.

Lillicrap , Jonathan J. Abstract : We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.

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We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving.

Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.

Submitted 29 February, ; v1 submitted 9 September, ; originally announced September Abstract : We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience.

Our distributed algorithm was applied to 49 games from Atari games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games. Submitted 16 July, ; v1 submitted 15 July, ; originally announced July Authors: Kamil Ciosek , David Silver.

Abstract : This paper presents a way of solving Markov Decision Processes that combines state abstraction and temporal abstraction. Specifically, we combine state aggregation with the options framework and demonstrate that they work well together and indeed it is only after one combines the two that the full benefit of each is realized.

We introduce a hierarchical value iteration algorithm where we first coarsely solve subgoals and then use these approximate solutions to exactly solve the MDP. This algorithm solved several problems faster than vanilla value iteration. Submitted 16 January, ; originally announced January Authors: Chris J. Abstract : The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge.

We train a large layer convolutional neural network by supervised learning from a database of human professional games. Submitted 10 April, ; v1 submitted 19 December, ; originally announced December Comments: Minor edits and included captures in Figure 2. Abstract : The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling.

We ask whether it is feasible and truly beneficial to combine rich probabilistic models with a closer approximation to fully Bayesian planning. First, we use a collection of counterexamples to show formal problems with the over-optimism inherent in Thompson sampling. Then we leverage state-of-the-art techniques in efficient Bayes-adaptive planning and non-parametric Bayesian methods to perform qualitatively better than both existing conventional algorithms and Thompson sampling on two contextual bandit-like problems. Submitted 9 February, ; originally announced February Comments: 11 pages, 11 figures.

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Authors: S. Branavan , David Silver , Regina Barzilay. Abstract : Domain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance.

Both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application.

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To effectively leverage textual information, our method automatically extracts the text segment most relevant to the current game state, and labels it with a task-centric predicate structure. This labeled text is then used to bias an action selection policy for the game, guiding it towards promising regions of the action space. We encode our model for text analysis and game playing in a multi-layer neural network, representing linguistic decisions via latent variables in the hidden layers, and game action quality via the output layer.

Operating within the Monte-Carlo Search framework, we estimate model parameters using feedback from simulated games. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Submitted 18 January, ; originally announced January Authors: Ti Wang , Daniel L. Abstract : This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities, or channels, such that input on one channel will correctly generate the associated response at the other and vice versa. In this way, the system develops a kind of supervised classification model meant to simulate aspects of human associative memory.

The DLA is trained on pairs of MNIST handwritten digit images to develop hierarchical features and associative representations that are able to reconstruct one image given its paired-associate. Experiments show that the multi-modal learning system generates models that are as accurate as back-propagation networks but with the advantage of a bi-directional network and unsupervised learning from either paired or non-paired training examples. Submitted 10 January, ; v1 submitted 20 December, ; originally announced December Comments: 9 pages, for ICLR Abstract : Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms.

A wide variety of such optimization algorithms have been devised; however, it is unclear whether these algorithms are robust and widely applicable across many different optimization landscapes. In this paper we develop a collection of unit tests for stochastic optimization. Each unit test rapidly evaluates an optimization algorithm on a small-scale, isolated, and well-understood difficulty, rather than in real-world scenarios where many such issues are entangled. Passing these unit tests is not sufficient, but absolutely necessary for any algorithms with claims to generality or robustness.

We give initial quantitative and qualitative results on numerous established algorithms. The testing framework is open-source, extensible, and easy to apply to new algorithms. Submitted 25 February, ; v1 submitted 20 December, ; originally announced December Abstract : We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

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The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. Submitted 19 December, ; originally announced December Authors: David Silver , Kamil Ciosek.

The contracts are standardized by a futures exchange as to quantity, quality, time and place of delivery. Only the price is variable. Hedgers use these contracts as a way to manage their price risk on an expected purchase or sale of the physical metal. They also provide speculators with an opportunity to participate in the markets by lodging exchange required margin. There are two different positions that can be taken: A long buy position is an obligation to accept delivery of the physical metal, while a short sell position is the obligation to make delivery.

Silver contracts are rarely settled in physical metal. The great majority of futures contracts are offset prior to the delivery date. For example, this occurs when an investor with a long position sells that position prior to delivery notice. There is usually a difference between the spot price of silver and the future price.

The future price, which we also display on this page, is used for futures contracts and represents the price to be paid on the date of a delivery of gold in the future. In normal markets, the futures price for gold is higher than the spot. The difference is determined by the number of days to the delivery contract date, prevailing interest rates, and the strength of the market demand for immediate physical delivery.

This is the change in the price of the metal from the previous close, which is not necessarily the previous day. Weekdays from PM NY time until midnight the previous close is from the current day. Kitco use the last quote at PM as the close of that given day. Change is always the difference between the current price and the price at pm. This is the change in the price of the metal from the price at the end of the previous trading session.

Currently, the weekday closing time is PM Eastern Time. This is the change in the price of the metal from 30 days ago as opposed from the previous close. This is the change in the price of the metal from a year ago today, as opposed from the previous close. Every precious metals market has a corresponding benchmark price that is set on a daily basis.

These benchmarks are used mostly for commercial contracts and producer agreements. These benchmarks are calculated partly from trading activity in the spot market. An OTC is not a formal exchange and prices are negotiated directly between participants with most of the transaction taking place electronically. Silver, actually trades 23 hours a day Sunday through Friday. Most OTC markets overlap each other; there is a one-hour period between 5 p. However, despite this one hour close, because spot is traded on OTC markets, there are no official opening or closing prices.

For larger transactions, most precious metals traders will use a benchmark price that is taken at specific periods during the trading day. The spread is the price difference between the bid and the ask price. Silver is a fairly liquid markets so traders can expect to see a fairly narrow spread in these markets; however, other precious metals may have wider spreads, reflecting a more illiquid marketplace.

Because there is no official closing or opening price for gold or silver, market participants rely on benchmark prices, set during different times of the day by different organizations. These benchmarks are also referred to as fixings. The silver benchmark price is determined daily in an electronic auction between participating banks with the LBMA, which is administered by ICE Benchmark Administration. Like the previous gold fix, the London Silver Fix was a global benchmark used for over years but was revamped in August The price is set daily in U.

The Kitco. These time zones have been selected because they represent the world's largest precious metals trading centers. One troy ounce of silver is the same around the world and for larger transaction are usually priced in U. Traditionally, currencies that are stronger than the U. While silver mostly quoted in ounces per U. Silver and most precious metals prices are quoted in troy ounces; however, countries that have adopted the metric system price gold in grams, kilograms and tonnes.

Though not as popular as kilograms and grams, Tael is a weight measurement in China. The tola is a weight measurement in South Asia. A troy ounce is used specifically in the weighing and pricing of precious metals and its use dates back to the Roman Empire when currencies were valued in weight. He is defeated and departs to continue training. The player battles Silver on Mt.

After the player has become Champion , Silver can indeed be found training in the Dragon's Den on Tuesday and Thursday, where he cannot be battled. On Monday and Wednesday, he will appear at Indigo Plateau and will challenge the player to a battle if they appear there. Moon giving him the chance to avenge his loss from earlier. In terms of personality, Silver is initially the darkest of the rivals in the series.

While most other rivals are generally good-natured, or at least zestful enough to crack a joke at the player, Silver is at first cruel, hateful, power-obsessed and even physically abusive, pushing the player character around several times. Even after he starts to become kinder, he remains a rather serious individual. Silver's hate for Team Rocket apparently stems from the failure of his father. He can be battled in White City once the player has cleared every Stadium Mode and the Gym Leader Castle , acting as the final opponent of the game.

Defeating Silver ends the game, and, in Round 1, also unlocks Round 2. If the player chose Chikorita :. If the player chose Cyndaquil :. If the player chose Totodile :. Silver played no major role in the anime, despite being a main character in the games. His only appearance in the anime was a brief cameo, in the original Japanese opening of The Legend of Thunder! A New Oath. In this opening, he was shown defeating Jimmy with his Nidoking , only to be defeated later on when Jimmy's Cyndaquil fully evolved into Typhlosion.

Also, Paul's official art shows him in the same pose as Silver does in one of his official artwork by Ken Sugimori. Silver appeared in The Legacy. He was seen meeting up with Looker outside the Indigo Plateau , discussing his relationship and last meeting with Giovanni that ultimately led to their estrangement. During the conversation, Silver asserts that he decided to stay out of his father's affairs, and he tells Looker that it is his job to find Giovanni before going off to face the Elite Four.

They share several similarities, such as stealing a starter from Professor Elm, in the manga's case, Totodile, owning a Sneasel, and being Giovanni 's son. Please remember to follow the manual of style and code of conduct at all times. Jump to: navigation , search. This article is about the character. The subject of this article has no official name. The name currently in use is a fan designator; see below for more information. Personal tools Create account Log in. Art from HeartGold and SoulSilver. Johto or originally Kanto , then moved to Johto.

Giovanni father. II , IV. The Legacy. Silver , Silver , Black , Tsubaki. Cherrygrove City. HeartGold and SoulSilver. Reward: Azalea Town.