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HDUOJ----2647Reward

Reward Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission compare their rewards ,and some one may have demands of the distributing of rewards ,just like a's reward b's.Dandelion's unclue wants to fulfill all the demands, of course ,he wants to use the least money.Every work's reward (n<=10000,m<=20000) then m lines ,each line contains two integers a and b ,stands for a's reward should

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拓扑排序-HDU2647 Reward

compare their rewards ,and some one may have demands of the distributing of rewards ,just like a’s reward ’s unclue wants to fulfill all the demands, of course ,he wants to use the least money.Every work’s reward (n<=10000,m<=20000) then m lines ,each line contains two integers a and b ,stands for a’s reward should

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    代码:Learning by Playing –Solving Sparse Reward Tasks from Scratch

    SAC-X algorithm enables learning of complex behaviors from scratch in the presence of multiple sparse reward Theory In addition to a main task reward, we define a series of auxiliary rewards. An important assumption is that each auxiliary reward can be evaluated at any state action pair. Minimize distance between lander craft and pad Main Task/Reward Did the lander land successfully (Sparse reward based on landing success) Each of these tasks (intentions in the paper) has a specific model

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    强化学习《奖励函数设计: Reward Shaping》详细解读

    Quick View Reward Shaping Intrinsically Motivated Reinforcement Learning Optimal Rewards and Reward Design Policy invariance under reward transformations: Theory and application to reward shaping[C]//ICML. 1999 Reward shaping via meta-learning[J]. arXiv preprint arXiv:1901.09330, 2019. 6.小结 关于Potential-based reward 作者检验了这么几类reward simple fitness-based reward functions,仅在fitness增加时给一个正奖励(也就是not Hungry状态给正奖励) fitness-based reward functions ,在fitness增加时给某个奖励,其他状态某个奖励 other reward functions,其他形式的奖励函数 ?

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    论文精读|4th|Deepmind新作|附下载|Solving Sparse Reward Tasks

    作者:Martin Riedmiller 、 Roland Hafner 、 Thomas Lampe等

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    强化学习系列案例 | 多臂老虎机问题策略实现

    return total_reward, expect_reward_estimate, operation 进行1000次游戏 N = 1000 total_reward,expect_reward += best_arm_reward return total_reward, expect_reward_estimate, operation 使用ε-greedy策略进行  += best_arm_reward         return total_reward, expect_reward_estimate, operation        使用Boltzmann 策略的累积奖励:", total_reward) # 期望奖励估计表 expect_reward_table = pd.DataFrame({         '期望奖励':expect_reward  += best_arm_reward         return total_reward, expect_reward_estimate, operation 进行1000次游戏 N = 1000

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    【强化学习】悬崖寻路:Sarsa和Q-Learning

    greedy next_obs, reward, done, _ = env.step(action) total_reward += reward obs (episode, ep_steps, ep_reward)) # 全部训练结束,查看算法效果 test_reward = test_episode(env, agent) print , next_obs, done) obs = next_obs # 存储上一个观察值 total_reward += reward total_steps (episode, ep_steps, ep_reward)) # 全部训练结束,查看算法效果 test_reward = test_episode(env, agent) print ('test reward = %.1f' % (test_reward)) 运行结果 参考资料 PARL强化学习公开课 强化学习之Q-learning与Sarsa算法解决悬崖寻路问题

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    设计模式第二讲--策略模式

    ; public class Reward { /** * 下单平台 0 web 1 小程序 2 app */ private int platform = 0 ; public Reward(int platform) { this.platform = platform; } /** * 给用户送积分 ; public class PayNotify { public static void main(String[] args) { Reward reward = new Reward(2); reward.giveScore(); } } 当我们的算法越来越多,越来越复杂的时候,并且当某个算法发生变化,将对所有其他计算方式都会产生影响,不能有效的弹性 ; import design.pattern.reward.score.AppScore; import design.pattern.reward.score.InterfaceScore; import

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    逆强化学习-学习人先验的动机

    practical application of reinforcement learning to real world problems is the need to specify an oracle reward Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function a limited set of demon- strations where it can be exceedingly difficult to unambiguously recover a reward While the exact reward function we provide to the robot may differ depending on the task, there is a can be interpreted as expressing a “locality” prior over reward function parameters.

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    强化学习(九)Deep Q-Learning进阶之Nature DQN

    这里给出我跑的某一次的结果: episode: 0 Evaluation Average Reward: 9.8 episode: 100 Evaluation Average Reward: 9.8 episode: 200 Evaluation Average Reward: 9.6 episode: 300 Evaluation Average Reward: 10.0 episode: Reward: 200.0 episode: 900 Evaluation Average Reward: 198.4 episode: 1000 Evaluation Average Reward Reward: 200.0 episode: 2200 Evaluation Average Reward: 200.0 episode: 2300 Evaluation Average Reward : 2800 Evaluation Average Reward: 200.0 episode: 2900 Evaluation Average Reward: 200.0     注意,由于DQN

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    强化学习(八)价值函数的近似表示与Deep Q-Learning

    next_state,reward,done,_ = env.step(action) # Define reward for agent reward = -1 if done Average Reward: 29.6 episode: 700 Evaluation Average Reward: 48.1 episode: 800 Evaluation Average Reward: 85.0 episode: 900 Evaluation Average Reward: 169.4 episode: 1000 Evaluation Average Reward: Reward: 200.0 episode: 2200 Evaluation Average Reward: 200.0 episode: 2300 Evaluation Average Reward : 2800 Evaluation Average Reward: 200.0 episode: 2900 Evaluation Average Reward: 200.0     大概到第1000

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    强化学习笔记5-PythonOpenAITensorFlowROS-阶段复习

    going forward give more reward then L/R ? , done, info = env.step(action) cumulated_reward += reward if highest_reward < cumulated_reward: highest_reward = cumulated_reward nextState = ''.join , done, info = env.step(action) cumulated_reward += reward if highest_reward < cumulated_reward: highest_reward = cumulated_reward nextState = ''.join

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    【深度强化学习】—— 入门

    He got a coin, that’s a +1 reward. Environment gives some reward R1 to the agent — we’re not dead (Positive Reward +1). of the expected return (expected cumulative reward). This means our agent cares more about the long term reward. of the expected cumulative reward.

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    强化学习系列(四)-PolicyGradient实例

    等信息 observation_, reward, done, info = env.step(action) # 将观测,动作和回报存储起来。 running_reward = ep_rs_sum else: # 累计每次探索的回报值 running_reward = running_reward * 0.99 + ep_rs_sum * 0.01 # reward大于阈值开始渲染,否则再学学 if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True print("episode:", i_episode, "rewards:", int(running_reward t时刻reward:当前t时刻reward * gamma + (t+1)时刻的reward

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    强化学习-A3C

    , worker_idx, global_ep_reward, result_queue, total_loss, num_steps): # 统计工具函数 if global_ep_reward == 0: global_ep_reward = episode_reward else: global_ep_reward = global_ep_reward * 0.99 + episode_reward * 0.01 print( f"{episode} | " f"Average Reward: {int(global_ep_reward = res_queue.get() if reward is not None: returns.append(reward) in memory.rewards[::-1]: # reverse buffer r reward_sum = reward + gamma * reward_sum

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    基于线性函数逼近的无奖励强化学习(CS AI)

    原文标题:On Reward-Free Reinforcement Learning with Linear Function Approximation 原文:Reward-free reinforcement During the exploration phase, an agent collects samples without using a pre-specified reward function After the exploration phase, a reward function is given, and the agent uses samples collected during We give an algorithm for reward-free RL in the linear Markov decision process setting where both the transition and the reward admit linear representations.

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    了解所学的奖励功能(CS LG)

    specify an RL agent's reward function. In such cases, a reward function must instead be learned from interacting with and observing humans. However, current techniques for reward learning may fail to produce reward functions which accurately Absent significant advances in reward learning, it is thus important to be able to audit learned reward In this paper, we investigate techniques for interpreting learned reward functions.

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    强化学习中的情景好奇心

    Curiosity can produce a reward bonus b_t which is further summed up with the task reward r_t to give anaugmented reward r'_t = r_t + b_t. There is a precious goal somewhere in the maze which would give a large reward. Experiments Task reward as a function of training step for VizDoom tasks: ? Reward as a function of training step for DMLab tasks: ?

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