Policy Gradient: Introduce Baseline Reduce variance by introducing a baselineb(s) ∇θEπθ[R(τ)]=Eπθ[∑t=0T−1(Gt−b(st))∇θlogπθ(at|st)] For any choice ofb(s),gradient estimator is unbiased. Near optimal choice is expected return b(st)≈E[rt+rt+1+⋯+rT−...
Policy gradient (PG) methods have been one of the most essential ingredients of reinforcement learning, with application in a variety of domains. In spite of the empirical success, a rigorous understanding of the global convergence of PG methods appears to be relatively lacking in the literature,...
很有意思的是,很多有结构性质的传统控制问题都是这样的,比如Linear Quadratic Control的linear policies,Optimal Stopping问题里的threshold policies和Inventory Control里的base-stock policy。非常有意思! 参考文献 1.^Agarwal, Alekh, et al. "Optimality and approximation with policy gradient methods in markov decis...
Value-based 方法:这类方法基于 value iteration 思想,直接利用 Bellman optimal equation 学习出最优动作价值函数 Q ∗ Q^* Q∗,进而导出最优策略 π ∗ \pi^* π∗ Policy Gradient 方法:这类方法基于 policy iteration 思想,交替执行 “利用 Bellman equation 评估当前策略” 和 “优化策略” 两步,直...
很有意思的是,很多有结构性质的传统控制问题都是这样的,比如Linear Quadratic Control的linear policies,Optimal Stopping问题里的threshold policies和Inventory Control里的base-stock policy。非常有意思! 参考文献 1.^Agarwal, Alekh, et al. 'Optimality and approximation with policy gradient methods in markov ...
在之前的Gradient ascent方法中都是选择了梯度的方向,如下图中的左图所示,但是更新步长如果选取地不好很容易掉入深渊: Intuition 在TRPO中限制了更新步长,并且在数学上证明了会收敛到局部最优或者全局最优: 在这里插入图片描述 参考文献:
While the behavior policy may not be optimal, it can be exploratory, and aids in the search for the optimal policy. Policy gradient algorithms [1], [2], [3], [4], [5], [6], [7], [8], [9] are a popular approach for solving MDPs. In a few special cases such as linear ...
Use favorite local or global optimizer to optimize simulated policy cost. If gradients are used, they are typically numerically estimated. Δp = -ε ∑x0w(x0)Vp 1st order gradient Δp = -(∑x0w(x0)Vpp)-1 ∑x0w(x0)Vp 2nd order Can we make model-based policy gradient more efficient...
, is exactly equal to the policy gradient, ! Try proving this, if you feel comfortable diving into the math. This approximate problem can be analytically solved by the methods of Lagrangian duality[1], yielding the solution: If we were to stop here, and just use this final result, the ...
策略梯度(Policy Gradient)是强化学习中的经典算法,通过采集得到的轨迹奖励来评估策略的期望回报,然后利用梯度上升直接优化策略往更高的回报方向更新。因此,PG 的性能与能否利用轨迹奖励准确评估当前策略对应期望回报有关。当任务的状态动作空间增大时,轨迹奖励就会有很多随机情况,不同情况获得的回报差异变大,评估策略期望...