强化学习-Vanilla Policy Gradient(VPG)

文章目录

• Background
• Quick Facts
• Key Equations
• Exploration vs. Exploitation
• Pseudocode
• Documentation
• Referances

Background

策略梯度背后的关键思想是提高导致更高回报的操作的概率，并降低导致低回报的操作的概率，直到获得最佳策略。

Quick Facts

• VPG 是一个on-policy算法
• VPG 能用于连续或者离散动作空间的环境
• 结合MPI可以有并行运算的VPG

Key Equations

令$π_θ$表示参数为 θ 的策略，$J(π_θ)$表示策略的有限步长无折扣收益的期望。 $J(π_θ)$的梯度为:$\nabla_{\theta} J(\pi_{\theta}) = \underset{\tau \sim \pi_{\theta}}E[{ \sum_{t=0}^{T} \nabla_{\theta} \log \pi_{\theta}(a_t|s_t) A^{\pi_{\theta}}(s_t,a_t) }]$其中$\tau$是一个轨迹，$A^{\pi_\theta}$是当前策略的优势函数。

策略梯度算法通过策略表现的随机梯度上升来更新策略参数：$\theta_{k+1} = \theta_k + \alpha \nabla_{\theta} J(\pi_{\theta_k})$尽管其他情况有使用有限步长无折扣策略梯度公式，策略梯度实现通常基于无限步长折扣收益来计算优势函数估计值。

Exploration vs. Exploitation

VPG以一种按on-policy方式训练随机策略。 这意味着它将根据最新版本的随机策略通过采样动作来进行探索。 动作选择的随机性取决于初始条件和训练程序。 在训练过程中，由于更新规则鼓励该策略利用已发现的奖励，因此该策略通常变得越来越少随机性。 这可能会导致策略陷入局部最优状态。

Pseudocode

Documentation

spinup.vpg(env_fn, actor_critic=, ac_kwargs={}, seed=0, steps_per_epoch=4000, epochs=50, gamma=0.99, pi_lr=0.0003, vf_lr=0.001, train_v_iters=80, lam=0.97, max_ep_len=1000, logger_kwargs={}, save_freq=10)
Parameters:

• env_fn – A function which creates a copy of the environment. The environment must satisfy the OpenAI Gym API.
• actor_critic – A function which takes in placeholder symbols for state, x_ph, and action, a_ph, and returns the main outputs from the agent’s Tensorflow computation graph:
• ac_kwargs (dict) – Any kwargs appropriate for the actor_critic function you provided to VPG.
• seed (int) – Seed for random number generators.
• steps_per_epoch (int) – Number of steps of interaction (state-action pairs) for the agent and the environment in each epoch.
• epochs (int) – Number of epochs of interaction (equivalent to number of policy updates) to perform.
• gamma (float) – Discount factor. (Always between 0 and 1.)
• pi_lr (float) – Learning rate for policy optimizer.
• vf_lr (float) – Learning rate for value function optimizer.
• train_v_iters (int) – Number of gradient descent steps to take on value function per epoch.
• lam (float) – Lambda for GAE-Lambda. (Always between 0 and 1, close to 1.)
• max_ep_len (int) – Maximum length of trajectory / episode / rollout.
• logger_kwargs (dict) – Keyword args for EpochLogger.
• save_freq (int) – How often (in terms of gap between epochs) to save the current policy and value function.

Referances

Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton et al. 2000

Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs, Schulman 2016(a)

Benchmarking Deep Reinforcement Learning for Continuous Control, Duan et al. 2016

High Dimensional Continuous Control Using Generalized Advantage Estimation, Schulman et al. 2016(b)

来源：CSDN

作者：Mystery_zu

链接：https://blog.csdn.net/anny0001/article/details/104045478