自然进化策略 natural evolution strategies (NES): NES 应该是这样推到过去的.∇ψEθ∼pψ(F(θ))=∇ψ(ΣθF(θ)∗pψ(θ))=(ΣθF(θ)pψ(θ)∗∇ψlogpψ(θ))=Eθ∼pψ(F(θ)∇ψlogpψ(θ)) 对均值方差就可以求导了,也即可以根据梯度来更新均值和方差 至此,NES就可以更新...
For MDP-based reinforcement learning algorithms, on the other hand, it is well known that frameskip is a crucial parameter to get right for the optimization to succeed. 对于基于MDP的强化学习算法,跳帧是算法优化的关键参数。 It is common practice in RL to have the agent decide on its actions ...
We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences. ...
https://openai.com/blog/evolution-strategies/ OpenAi发的论文,用于替代强化学习,与传统Qlearning方式不同的是,不用计算梯度,直接改成了一个优化方法。(回归了上大学的时候数学建模的一些方法)。直接上算法,很简单。 实际使用中,初始化提前在每个worker中生成好一些disturbs,然后每次随机一个数,作为下标,去选取distu...
2 Evolution Strategies 2.1 Scaling and parallelizing ES 2.2 The impact of network parameterization 3 Smoothing in parameter space versus smoothing in action space 3.1 When is ES better than policy gradients? 3.2 Problem dimensionality 3.3 Advantages of not calculating gradients ...
它的性质是 greedy,它只保留最佳解决方案,抛弃了此外的所有解决方案, 这个算法在更复杂的问题中很容易陷入局部最优。 学习资料: http://blog.otoro.net/2017/10/29/visual-evolution-strategies/ Practical Reinforcement Learning 也许可以找到你想要的:
A side-effect of these trends has been that, over the last 15 years, reinforcement learning (RL) algorithms have become more and more similar to evolution strategies such as (μW , λ)-ES and CMA-ES. Evolution strategies treat policy improvement as a black-box optimization problem, and ...
OpenAI 发表了一篇论文:Evolution Strategies as a Scalable Alternative to Reinforcement Learning Evolution Strategies 的数据效率虽然没有 RL 高,但却有许多好处。 因为放弃了梯度计算,所以算法评估起来更有效。 而且可以很容易地将 ES 算法的计算分配给数千台计算机进行并行计算。
论文Evolution Strategies as a Scalable Alternative to Reinforcement Learning 要点¶ 上节内容里, 我们见到了使用 NEAT 来进化出一个会立杆子的机器人. 这次, 我们使用另一种进化算法 Evolution Strategy (后面都用简称 ES 代替) 来实现大规模强化学习. 如果你的计算机是多核的, 我们还能将模拟程序并行到你多个...
Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient, and are much faster when many central processing units (CPUs) are available due to better parallel...