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CSE510 Deep Reinforcement Learning (Lecture 14)

Advanced Policy Gradient Methods

Trust Region Policy Optimization (TRPO)

"Recall" from last lecture

\max_{\pi'} \mathbb{E}_{s\sim d^{\pi},a\sim \pi} \left[\frac{\pi'(a|s)}{\pi(a|s)}A^{\pi}(s,a)\right]

such that

\mathbb{E}_{s\sim d^{\pi}} D_{KL}(\pi(\dot|s)||\pi'(\dot|s))\leq \delta

Unconstrained penalized objective:

d^*=\arg\max_{d} J(\theta+d)-\lambda(D_{KL}\left[\pi_\theta||\pi_{\theta+d}\right]-\delta)

\theta_{new}=\theta_{old}+d

First order Taylor expansion for the loss and second order for the KL:

\approx \arg\max_{d} J(\theta_{old})+\nabla_\theta J(\theta)\mid_{\theta=\theta_{old}}d-\frac{1}{2}\lambda(d^T\nabla_\theta^2 D_{KL}\left[\pi_{\theta_{old}}||\pi_{\theta}\right]\mid_{\theta=\theta_{old}}d)+\lambda \delta

If you are really interested, try to fill the solving the KL Constrained Problem section.

Natural Gradient Descent

Setting the gradient to zero:

\begin{aligned}
0&=\frac{\partial}{\partial d}\left(-\nabla_\theta J(\theta)\mid_{\theta=\theta_{old}}d+\frac{1}{2}\lambda(d^T F(\theta_{old})d\right)\\
&=-\nabla_\theta J(\theta)\mid_{\theta=\theta_{old}}+\frac{1}{2}\lambda F(\theta_{old})d
\end{aligned}

d=\frac{2}{\lambda} F^{-1}(\theta_{old})\nabla_\theta J(\theta)\mid_{\theta=\theta_{old}}

The natural gradient is

\tilde{\nabla}J(\theta)=F^{-1}(\theta_{old})\nabla_\theta J(\theta)

\theta_{new}=\theta_{old}+\alpha F^{-1}(\theta_{old})\hat{g}

D_{KL}(\pi_{\theta_{old}}||\pi_{\theta})\approx \frac{1}{2}(\theta-\theta_{old})^T F(\theta_{old})(\theta-\theta_{old})

\frac{1}{2}(\alpha g_N)^T F(\alpha g_N)=\delta

\alpha=\sqrt{\frac{2\delta}{g_N^T F g_N}}

However, due to the quadratic approximation, the KL constrains may be violated.

Linear search

We do Linear search for the best step size by making sure that

• Improving the objective
• Satisfying the KL constraint

TRPO = NPG + Linesearch + monotonic improvement theorem

Summary of TRPO

Pros

• Proper learning step
• Monotonic improvement guarantee

Cons

• Poor scalability
  • Second-order optimization: computing Fisher Information Matrix and its inverse every time for the current policy model is expensive
• Not quite sample efficient
  • Requiring a large batch of rollouts to approximate accurately

Proximal Policy Optimization (PPO)

Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. -- OpenAI

link to paper

Idea:

• The constraint helps in the training process. However, maybe the constraint is not a strict constraint:
• Does it matter if we only break the constraint just a few times?

What if we treat it as a “soft” constraint? Add proximal value to the objective function?

PPO with Adaptive KL Penalty

\max_{\theta} \hat{\mathbb{E}_t}\left[\frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{old}}(a_t|s_t)}\hat{A}_t\right]-\beta \hat{\mathbb{E}_t}\left[KL[\pi_{\theta_{old}}(\dot|s_t),\pi_{\theta}(\dot|s_t)]\right]

Use adaptive \beta value.

L^{KLPEN}(\theta)=\hat{\mathbb{E}_t}\left[\frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{old}}(a_t|s_t)}\hat{A}_t\right]-\beta \hat{\mathbb{E}_t}\left[KL[\pi_{\theta_{old}}(\dot|s_t),\pi_{\theta}(\dot|s_t)]\right]

Compute d=\hat{\mathbb{E}_t}\left[KL[\pi_{\theta_{old}}(\dot|s_t),\pi_{\theta}(\dot|s_t)]\right]

• If d<d_{target}/1.5, \beta\gets \beta/2
• If d>d_{target}\times 1.5, \beta\gets \beta\times 2

PPO with Clipped Objective

\max_{\theta} \hat{\mathbb{E}_t}\left[\frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{old}}(a_t|s_t)}\hat{A}_t\right]

r_t(\theta)=\frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{old}}(a_t|s_t)}

• Here, r_t(\theta) measures how much the new policy changes the probability of taking action a_t in state s_t:
  • If r_t > 1 :the action becomes more likely under the new policy.
  • If r_t < 1 :the action becomes less likely.
• We'd like r_tA_t to increase if A_t > 0 (good actions become more probable) and decrease if A_t < 0.
• But if r_t changes too much, the update becomes unstable, just like in vanilla PG.

We limit r_t(\theta) to be in a range:

L^{CLIP}(\theta)=\hat{\mathbb{E}_t}\left[\min(r_t(\theta)\hat{A}_t, clip(r_t(\theta), 1-\epsilon, 1+\epsilon)\hat{A}_t)\right]

Trusted region Policy Optimization (TRPO): Don't move further than \delta in KL. Proximal Policy Optimization (PPO): Don't let r_t(\theta) drift further than \epsilon.

PPO in Practice

L_t^{CLIP+VF+S}(\theta)=\hat{\mathbb{E}_t}\left[L_t^{CLIP}(\theta)+c_1L_t^{VF}(\theta)+c_2S[\pi_\theta](s_t)\right]

Here L_t^{CLIP}(\theta) is the surrogate objective function.

L_t^{VF}(\theta) is a squared-error loss for "critic" (V_\theta(s_t)-V_t^{target})^2.

S[\pi_\theta](s_t) is the entropy bonus to ensure sufficient exploration. Encourage diversity of actions.

c_1 and c_2 are trade-off parameters, in paper c_1=1 and c_2=0.01.

Summary for Policy Gradient Methods

Trust region policy optimization (TRPO)

• Optimization problem formulation
• Natural gradient ascent + monotonic improvement + line search
• But require second-order optimization

Proximal policy optimization (PPO)

• Clipped objective
• Simple yet effective

Take-away:

• Proper step size is critical for policy gradient methods
• Sample efficiency can be improved by using important sampling