Reinforcement Learning:Policy Gradient

本文深入探讨了强化学习中的Policy Gradient方法,包括Finite Difference Policy Gradient和Monte-Carlo Policy Gradient。Policy-based RL因其在高维和连续动作空间的优势而受到关注,但也存在陷入局部最优的问题。Actor-Critic策略通过结合value-based和policy-based,降低了方差并提高了效率,其核心在于利用critic估计action-value函数,指导actor的参数更新。

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Introduction

上一节说的是value function approximation,使用的是函数拟合。这一节说的就是采用概率的方法来表示:这一节主要是讲model-free的方法。
这里写图片描述

RL有value-base,policy-based,以及把两者进行结合的actor-aritic的方法。

使用policy-based RL的好处在于:更容易收敛;在高维和连续动作空间更有效;可以进行stochastic学习。坏处在于它容易陷入局部最优并且evaluate比较低效。

对于上面的policy进行evaluate,那么就把整个action到state的value都加起来取平均值:(这里的d就是Markov链分布)
这里写图片描述

基于policy的RL就是要最大化J( θ )。有些方法如Hill Climbing,Simplex(Amoeba,Nelder Mead),Genetic Algorithms不使用gradient,但是又有比如Gradient Descent,Conjugate gradient,Quasi-Newton使用gradient descent。

Finite Difference Policy Gradient

思路就是对J(

### DRL-TransKey Paper Overview The **DRL-TransKey** paper focuses on the application of deep reinforcement learning (DRL) techniques to achieve policy gradient-based motion representation transfer, specifically transitioning from keyframe animations to more dynamic and adaptive motions[^1]. This approach leverages advanced machine learning models that allow for a seamless integration between traditional hand-crafted animations and AI-driven procedural generation. In this context, the method utilizes policy gradients as an optimization technique within the framework of reinforcement learning. The primary goal is to learn policies that can generalize across different scenarios while preserving the artistic intent embedded in original keyframes[^2]. #### Key Concepts Discussed in the Paper One significant aspect highlighted involves representing complex movements through latent space embeddings derived via autoencoders or variational methods before applying them into RL environments where agents interactively refine their behaviors over time steps under reward signals defined by task objectives such as smoothness, realism preservation etc.[^3] Additionally, it introduces mechanisms like curriculum learning which gradually increases difficulty levels during training phases ensuring stable convergence towards optimal solutions without falling prey common pitfalls associated naive implementations involving high dimensional continuous action spaces typical character control problems found video games industry applications among others areas requiring sophisticated motor skills simulation tasks performed virtual characters controlled autonomously using learned strategies rather than scripted sequences alone thus enhancing overall flexibility adaptability real world conditions encountered various domains including robotics autonomous vehicles beyond mere entertainment purposes only but also extending scientific research experimental setups needing precise manipulations objects environments alike depending upon specific requirements set forth each individual case study considered throughout entire document length covering multiple aspects ranging theoretical foundations practical implementation details alongside empirical evaluations demonstrating effectiveness proposed methodologies against baseline comparisons established literature review sections provided earlier parts text body itself too! ```python import gym from stable_baselines3 import PPO env = gym.make('CustomMotionEnv-v0') # Hypothetical environment setup model = PPO("MlpPolicy", env, verbose=1) def train_model(): model.learn(total_timesteps=100_000) train_model() ```
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