#mountaincar

[mountainstartransportation-blog]

Uber from Denver to Aspen

Nowadays, Mountain Star Transportation is very reliable for providing uber services from Denver international airport to Aspen for his cars. It does all to be your first choice for the car service. We are beneficial for all people around for booking their cabs at reasonable rates.

[myprogrammingsolver]

Reinforcement Learning Assignment 4 Solution

Reinforcement Learning Assignment 4 Solution

Experiment Description   Programming language: python3   You should compare the performance of DQN and one kind of improved DQN and test them in a classical RL control environment{MountainCar. OPENAI gym provides this environment, which is implemented with python (https://gym.openai.com/envs/MountainCar-v0/). What’s more, gym also provides other more complex environment like atari games and…

[silvermountainexpress]

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[eurekakinginc]

"[P] I used reinforcement learning to solve Numberphile's "cat and mouse" game!"- Detail: Here's a fun gif of it successfully escaping after a couple attempts!I used the DDPG an A2C reinforcement learning algorithms to train the agent to solve this puzzle I saw in a Numberphile video. While the puzzle itself is pretty simple, using RL to solve it was somewhat tricky. Like MountainCar, the reward space is pretty sparse, but effectively made much sparser because of the cat.To get it to solve the more challenging versions (i.e., with a faster cat), I had to "bootstrap" (not in the traditional RL sense) by starting with the solution of the easier problem (which is definitely kind of cheating :P).Here's the full blog post I did on it. If anyone has any feedback or questions, please let me know!. Caption by diddilydiddilyhey. Posted By: www.eurekaking.com

[topfundedprojects]

Tweeted

startupweek "RT hackernoon: INNOVATION AT ALTITUDE. Join us tonight at the COSnowsportsMus tonight at 6pm to talk about building a tech startup in the rocky mountains. MountainCareers techstars startupweek davidsmooke linhdaosmooke #startuplife https://t.co/QSxeupDXmx"

— topfundedprojects (@topfundedideas) June 6, 2019

[omarkhanca]

machine learning > [ML] Q-Learning Algorithm Solution for MountainCar-v0 | 2018-08-23T00:59:57.000Z

[study-iris1022]

MountainCar問題をQ-Learning + Multi step Learningを使って解いてみた

最近忙しいのと論文全然読み解けないしいろいろと進歩してねえなということで簡単に実装できそうな問題に逃げてまいりました。

最近の試みは以下。

Double DQN -> そんなに変わらん

Dueling DQN -> どうやってもlossが発散するんですけど？

Priorized Memory -> ネット上のやつを参考にしたけどこれで実装うまくいってるかよくわからない。あと学習の後半になると頭打ちになる

ということで現実逃避中です。各手法の解説はこことか見てください。

さて、この記事でやりたいことは簡単で、A3C(Asynchronous Advanced Actor-Critic)と呼ばれる強化学習の手法があるのですが、それに少しずつ迫っていこうという試みです。

A3Cの中ではMulti Step Learningと言われるTD誤差の拡張式がロスの計算に用いられています。これまではQ値の更新時には[そのステップで取った行動に基づく報酬] + ...という式がありましたが、[そのステップで取った行動に基づく報酬]を拡張し、[そのステップで取った行動に基づく報酬 + (n-1)Step先の実際に取った行動に基づくまでの割引報酬]にしてやろうというのがこの手法です。

ソースコードは以下。例によってjupyter notebookで実行してたので...

import numpy as np import gym env = gym.make('MountainCar-v0') obs = env.reset() def get_descretization_observation(env, obs, elem_num = 20): lows = np.array(env.observation_space.low, dtype=np.float64) highs = np.array(env.observation_space.high, dtype=np.float64) diffs = (highs - lows) / elem_num indices = np.array( np.divide((obs - lows), diffs) , dtype=np.int) return indices gamma = 0.99 #時間割引率 class ReplayMemory: def __init__(self, maxlen): self.maxlen = maxlen #s,a,r,s,1.0-done self.state = np.empty(shape=maxlen, dtype=np.object) self.action = np.zeros(shape=maxlen, dtype=np.int) self.reward = np.zeros(shape=maxlen, dtype=np.float) self.next_state = np.zeros(shape=maxlen, dtype=np.object) self.continues = np.empty(shape=maxlen, dtype=np.float) self.index = 0 self.length = 0 self.gamma = gamma def append(self, data): self.state[self.index] = data[0] self.action[self.index] = data[1] self.reward[self.index] = data[2] self.next_state[self.index] = data[3] self.continues[self.index] = data[4] self.length = min(self.length + 1, self.maxlen) self.index = (self.index + 1) % self.maxlen def sample(self, nstep): r = 0 for i in range(nstep): r += self.reward[(self.index - 1)- i] * (self.gamma ** (i)) s = self.state[(self.index - 1) - (nstep - 1)] a = self.action[(self.index - 1) - (nstep - 1)] ns = self.next_state[self.index - 1] return (s, a, r, ns) replay_memory_size = 100000 replay_memory = ReplayMemory(replay_memory_size) alpha = 0.1 #学習率 def update_Q_table(qtable, memory, nstep): state_indices, action, reward, next_state_indices = memory.sample(nstep) next_max_qvalue = max(qtable[next_state_indices[0], next_state_indices[1]]) qvalue = qtable[state_indices[0], state_indices[1], action] qtable[state_indices[0], state_indices[1], action] = \ qvalue + alpha * (reward - qvalue + (gamma ** nstep) * next_max_qvalue) def get_action_by_epsilon_greedy(env, qtable, obs, episode, epsilon = 0.05): if(np.random.uniform(0, 1) > epsilon): obs_indices = get_descretization_observation(env, obs) action = np.argmax(qtable[obs_indices[0], obs_indices[1]]) else: #random action action = np.random.choice([0,1,2]) return action

max_episode = 2000 max_step = 200 nstep = 3 #multi step q learningでどれだけ先まで見るか qtable = np.zeros((20, 20, 3)) rewards = [] #学習 for m in range(max_episode): #initialize total_reward = 0 obs = env.reset() for t in range(max_step): #状態S_tにて政策piに基づき行動A_iを選択(epsilon-greedy) act = get_action_by_epsilon_greedy(env, qtable, obs, m) #状態S_tおよび報酬R_tを観測 next_obs, reward, done, info = env.step(act) #過去のs, a, r, s_, 1.0 - doneを覚えておく obs_indices = get_descretization_observation(env, obs) next_obs_indices = get_descretization_observation(env, next_obs) replay_memory.append((obs_indices, act, reward, next_obs_indices, 1.0 - done)) #価値観数の更新 if(nstep < t): update_Q_table(qtable, replay_memory, nstep) total_reward += reward obs = next_obs if done: rewards.append(total_reward) #print("Done") break

まぁ前と比較してQ値の更新が少し変わってるだけです。あとMemoryクラスがありますがこれは将来的に手抜きをするための布石であり、必ずしも必要ではありません。

nstepを少し変えてみた時のScoreが以下。スコアは( 最大エピソード数 + 累積(割引なし)報酬)で計算してあります。MountainCar問題はゴールしていないときに-1の報酬が割り当てられる問題なので、グラフがマイナスなのが気持ち悪かった。

こうしてみると、最適なnは実験的に求めるよりほかなく、それも割と大変だと思います。迷ったらn=3かなぁ。欲張りにn=5としてもいいけど。

[cougaronfire]

Where to go in June? Every month, our travel editorial team will present selected excursions. Four travel tips from the North Sea to Constance

Walk from Hooge to Pellworm

Admittedly, wading is no longer considered an innovation, but it is a great way to start the summer. There are many expeditions of the Wattwanderungen: short and long tours, for families and sportsmen. But, which few know, there are also special tours every year, which are only offered on a few dates. The hike from Hooge to Pellworm counts in 2017. What makes this tour so special? It goes from Hallig to the island, which is the first special feature. It is also a journey back in time into a lost world. Destroyed and drowned in a murderous hurricane flood a few centuries ago - the traces and memories are often seen. Likewise, birds can be observed, and with some luck also seals. And finally,

The only date for this special tour in June is 12.06.2017. It will be followed by the end of September. 22 euros incl. Return from Pellworm to Hooge.for ��further information at: viagens.

Adrenaline at the Ruhr Source

The last snow has long since melted and now the mountaincars can be run in the ski resort Ruhrquelle, not far from Winterberg. The three-wheeled vehicles are similar to GoKarts only with an ergonomic seat position. The route at the Ruhrquelle is the largest mountaincar facility north of Bavaria with a length of 300 meters and seven curves. The riders return the mountain ride with the chairlift. Arrived at the top, it says: Take a seat, brake and go downhill. The mountaincars function completely without an engine, as well as the helmet and the robust construction, the hydraulic disc brakes also provide safety.

The complex is open during the summer months from Thursday to Sunday. Children from a body height of 1.30 meters are allowed to drive their own mountaincar. All information is available online: dicas de viagens

En route on the Mühlenwanderweg

The classics of the hiking trails in the Rheingau such as Rheinstieg and Hildegardweg are now well-known, but there are undiscovered hiking pearls like the Mühlenwanderweg in the Elsterbachtal. The walk takes almost two hours and leads from the Oestrich-Winkeler district of Winkel to 5.5 kilometers to the monastery Marienthal. A permanent companion is the Elsterbach, on whose bank mills once rattled. They were the driving force behind economic development in the region. Today, old millstones adorn the entrances to the former mills along the way, infotafes provide information on historical background. In addition, hikers on the Mühlenweg always enjoy fantastic views and the silence of the Rheingau.

What a theater in Konstanz

The Freilichttheater on the Münsterplatz in Münster celebrates its 10th anniversary in 2017. In the focus: the Swiss national mythos around Wilhelm Tell and the Rütlischwur. Staged by the Theater Konstanz, theaters between June 23 and July 27, 2017 can experience Friedrich Schiller's famous drama "Wilhelm Tell" in a unique setting on the historic Münsterplatz. But this year, not only professional actors and actresses are on the stage, but also many Constance citizens. They should thus continue the tradition of the "bourgeois theater".

please visit web portal: viagem

[fiatpanda]

Best mountain car ever built

[mountainstartransportation-blog]

Uber from Denver to Aspen

https://mountaincars.com/Uber-from-Denver-to-Vail

Nowadays, Mountain Star Transportation is very reliable for providing uber services from Denver international airport to Aspen for his cars. It does all to be your first choice for the car service. We are beneficial for all people around for booking their cabs at reasonable rates.

[mountainstartransportation-blog]

Transportation from vail to denver

https://mountaincars.com/

Mountain Star Transportation provides private shuttle service by trained drivers and makes your transportation prompt, safe & comfortable. Get Denver to Vail transportation, car service, and limo at mountaincars.com.

[mountainstartransportation-blog]

Car Service from Denver to Steamboat Springs

https://mountaincars.com/car-service-from-Denver-to-Steamboat-Springs

Get Denver to Vail and Denver to Steamboat Springs transportation, car service in Uber at mountaincars.com. We provide private Uber service by professional drivers and make your transportation prompt, safe & comfortable.

[mountainstartransportation-blog]

Transportation from Denver to Vail

https://mountaincars.com/

 Mountain Star Transportation provides private shuttle service by trained drivers and makes your transportation prompt, safe & comfortable. Get in touch with us on Mountaincars.com.

 #mountaincars

[eurekakinginc]

"[D] why the same reinforcement learning algorithm worked for MountainCar, but does not work for LunarLander (and others)"- Detail: Hi Reddit community, I'm currently self-learning/exploring reinforcement learning. I have downloaded a few codes to try out and to get a feel of the code. There is a piece of code [code A] about using A3C for CartPole-v0, and it manages to learn very well. And another piece of code [code B] that uses DQN for LunarLander-v2, it managed to train a smart agent too.Then I change the environment in code A (uses A3C) to LunarLander-v2 and MountainCar-v0, there weren't any errors, but the agent fails to learn. Likewise, I change the environment in code B (uses DQN) to CartPole-v0 and MountainCar-v0, it didn't learn as well.Why is it so? Is it because different environments have different rewards system? Or the hyperparameters that worked for CartPole-v0 does not work for LunarLander-v2?. Caption by ErmJustSaying. Posted By: www.eurekaking.com

[omarkhanca]

machine learning > [ML] Q-Learning Algorithm Solution for MountainCar-v0 | 2018-08-23T00:59:57.000Z