Which policy gradient algorithm are you using?

So, you probably all know the formula for updating the policy network using the policy gradient theorem:

$\nabla_{θ} J (θ) = E_{a, s \sim π_{θ}} [\nabla \log π_{θ} (a | s) R (s, a)] .$

Here, the action $a$ is drawn from the current policy $π_{θ}$ . The state $s$ is sampled from the state distribution function of the environment by following $π_{θ}$ . And $R (s, a)$ is the return obtained for the state $s$ if you select action $a$ and would continue to follow the policy $π_{θ}$ . In order to update, you perform a rollout with your current policy, you estimate the gradient using this formula, and you backpropagate. I will not go into detail where this formula comes from, but if you want to read more you can check out this comprehensive blog post from Lilian Weng.

You’ve probably also heard that there are different algorithms for computing the return $R$ , e.g. Monte-Carlo, actor-critic, A2C, GAE, etc. What we will do is start from the most basic algorithm - vanilla policy gradient, and continuously improve on it until we arrive at the most successful and widely used algorithm today - PPO. Each agent that we implement will conform to the following api:

class Agent:
    def __init__(self, policy_network, value_network):
        self.policy_network = policy_network
        self.value_network = value_network

    @torch.no_grad()
    def policy(self, s):
        return Categorical(logits=self.policy_network(s))

    @torch.no_grad()
    def value(self, s):
        return self.value_network(s)

    def update(self, states, actions, rewards):
        raise RuntimeError("not implemented")

Disclaimer: The code snippets given in this blog post will probably not work directly out of the box. The full working code as well as results on training these agents on the Atari game LunarLander can be seen on github.

VANILLA POLICY GRADIENT⌗

In vanilla policy gradient (VPG) there is nothing fancy; we just calculate the return $R$ as a Monte-Carlo estimate. We perform a full episode rollout and we collect the states, actions, and rewards ( $s_{t}$ , $a_{t}$ , $r_{t + 1}$ ) at each step until we reach a terminal state. Once we’ve reached the terminal state we can estimate the return for each of the visited states:

$R_{t} = \sum_{i = t}^{T} r_{i + 1} .$

Finally we compute the gradient:

$\nabla_{θ} J (θ) = \frac{1}{T} \sum_{t = 1}^{T} \nabla \log π_{θ} (a_{t} | s_{t}) R_{t} .$

Each of the states was visited using the current policy and the state distribution function of the environment, so for each state-action pair we can compute an estimate of the gradient. Then, we average over the samples, much like a mini-batch update in supervised learning.

To implement this algorithm we will first make use of a function that describes an agent-environment loop. We will assume that env is a non-vectorized environment that conforms to the OpenAI Gym API.

def environment_loop(agent, env, num_iters):
    for _ in range(num_iters):
        states, actions, rewards = [], [], []
        s, _ = env.reset()
        done = False
        while not done:
            states.append(s)
            act = agent.policy(s).sample() # policy() returns a distribution
            s, r, t, tr, _ = env.step(acts)
            actions.append(act)
            rewards.append(r)
            done = (t | tr)

        agent.update(np.array(states), np.array(actions), np.array(rewards))

The environment loop will run multiple iterations and on each iteration it will rollout an episode using the current policy, and then it will update the policy using the sampled data.

class VPGAgent(Agent):
    def update(self, states, actions, rewards):
        T = rewards.shape[0]
        returns = rewards @ np.tril(np.ones(T))
        returns = (returns - returns.mean()) / (returns.std() + 1e-8)
        logits = self.policy_network(states)
        logp = F.cross_entropy(logits, actions, reduction="none")
        loss = (logp * returns).mean()

        self.optim.zero_grad() # assume optim is defined in agent.__init__()
        loss.backward()
        self.optim.step()

The returns are calculated by simply adding the rewards obtained from the given time-step onwards. I’ve used a simple vector-matrix multiplication here where I multiply the rewards vector by a lower-triangular matrix of ones in order to get the desired sums.

One trick that is usually done in practice is to normalize the returns before computing the gradient. This is done in order to stabilize the training of the neural network. Note that this modification does not change the expectation of the gradient estimation.

VPG WITH BASELINE⌗

Since the single-sample Monte-Carlo estimate of the returns might have a lot of variance, we may want to perform multiple rollouts instead of just one. That is, rollout several episodes and only then calculate the gradient and backpropagate. However, it is not clear how helpful that would be because you will still get (mostly) single-sample Monte-Carlo estimates; it’s just that you will have more data points in the batch. To see why is that, consider rolling out a second episode with the same policy. At some step $t_{d}$ your second rollout will diverge from the first rollout and from then onward you will sample new states and for the returns of these states you will have single-sample estimates. Only for the states before step $t_{d}$ you will have a more accurate estimate.

A much better approach to reduce the variance of the estimation would be to add a baseline to the estimate of the return.

We will baseline the returns using the value function $V^{π} (s)$ . As a side note: the best baseline in terms of variance reduction¹ is actually:

$E_{a \sim π_{θ}} [\frac{\nabla \log^{2} π_{θ} (a | s) R (s, a)}{\nabla \log^{2} π_{θ} (a | s)}],$

and the baseline that we are using is $V (s) = E [R (s, a)]$ .

The formula for the gradient now becomes:

$\begin{aligned} \nabla_{θ} J (θ) & = E_{a, s \sim π_{θ}} [\nabla \log π_{θ} (a | s) R (s, a) - V (s)] \\ = E_{a, s \sim π_{θ}} [\nabla \log π_{θ} (a | s) A (s, a)], \end{aligned}$

where $A (s, a)$ is the advantage, describing how much better it is to take action $a$ compared to the other actions.

The value function is approximated using a second neural network which is trained concurrently with the policy. The update function will be modified like this:

class VPGAgent(Agent):
    def update(self, states, actions, rewards):
        T = rewards.shape[0]
        returns = rewards @ np.tril(np.ones(T))
        adv = returns - self.value(states)

        # Update the policy network.
        adv = (adv - adv.mean()) / (adv.std() + 1e-8)
        logits = self.policy_network(states)
        logp = F.cross_entropy(logits, actions, reduction="none")
        loss = (logp * adv).mean()

        self.policy_optim.zero_grad() # assume defined in agent.__init__()
        loss.backward()
        self.policy_optim.step()

        # Update the value network.
        for o, r in DataLoader((states, returns), self.batch_size):
            pred = self.value_network()
            vf_loss = F.mse_loss(pred, r)
            self.value_optim.zero_grad() # assume defined in agent.__init__()
            vf_loss.backward()
            self.value_optim.step()

Note that even though we baseline the returns we are still normalizing the advantages after that. This effectively means that we are using a different baseline and not $V (s)$ , but as already explained this will not change the expectation of the gradient. It will simply improve the training of our network.

ONLINE ADVANTAGE ACTOR-CRITIC⌗

Now that we have a value network we can actually compute the return by bootstrapping instead of using a Monte-Carlo estimate:

$R (s_{t}, a_{t}) = r_{t + 1} + V (s_{t + 1}) .$

This is the so-called actor-critic setup, where we have an actor (the policy network) that selects actions to perform the rollout and a critic (the value network) that is used to compute the returns, i.e. it grades the performance.

Combining the actor-critic with a baseline we get the advantage actor-critic (A2C²). The formula for the gradient now becomes:

$\nabla_{θ} J (θ) = E_{a, s, r \sim π_{θ}} [\nabla \log π_{θ} (a | s) (r + V (s^{'}) - V (s))] .$

Here $r$ is the reward obtained when performing action $a$ , and $s^{'}$ is the next state of the environment.

With this setup we actually don’t have to run episodes until the end. In fact we can update the policy (and the value network) at every single step. But that is not a very good idea because we will be estimating the gradient using a single sample. Instead, what we could do is:

either run multiple environments in parallel in order to obtain multiple samples at every step,
or rollout the episode for several steps and only then perform the update.

We will actually do both.

def environment_loop(agent. env, num_iters, steps):
    num_envs = env.num_envs
    s, _ = env.reset()

    states = np.zeros(
        shape=(steps, num_envs, *env.single_observation_space.shape),
        dtype=np.float32,
    )
    next_states = np.zeros_like(states)
    actions = np.zeros(shape=(steps, num_envs), dtype=int)
    rewards = np.zeros(shape=(steps, num_envs), dtype=np.float32)
    done = np.zeros(shape=(steps, num_envs), dtype=bool)

    for _ in range(num_iters):
        for i in range(steps):
            states[i] = s
            acts = agent.policy(s).sample()
            s, r, t, tr, info = env.step(acts)

            actions[i] = acts
            rewards[i] = r
            next_states[i] = s
            done[i] = (t | tr)

        agent.update(states, actions, rewards, next_states, done)

We will now assume that env is a vectorized environment. The environment loop will run multiple iterations and on each iteration it will rollout the policy for a fixed amount of steps. Note that vectorized environments will auto-reset any sub-environment that is terminated or truncated. This means that a given segment of experiences might contain data from multiple episodes, and for this reason we will use the done tensor to indicate which of the states are terminal states.

Once we update, we continue stepping the vectorized environment from where we left off, i.e. the environment is not reset at the beginning of the new iteration. This is actually very helpful in the case of long horizon tasks where episodes could be extremely long (think 100K steps).

The update function is not much different, but note that it now accepts additional parameters:

class A2CAgent(Agent):
    def update(self, states, actions, rewards, next_states, done):
        values = self.value(states)
        next_values = self.value(next_states)
        next_values = np.where(done, 0., next_values)
        returns = rewards + next_values
        adv = rewards + next_values - values

        # Update the policy network.
        adv = (adv - adv.mean()) / (adv.std() + 1e-8)
        logits = self.policy_network(states)
        logp = F.cross_entropy(logits, actions, reduction="none")
        loss = (logp * adv).mean()

        self.policy_optim.zero_grad()
        loss.backward()
        self.policy_optim.step()

        # Update the value network.
        for o, r in DataLoader((states, returns), self.batch_size):
            pred = self.value_network()
            vf_loss = F.mse_loss(pred, r)
            self.value_optim.zero_grad()
            vf_loss.backward()
            self.value_optim.step()

Here we can see how we are actually using the information provided by done: in case our agent steps a sub-environment from its current state into a terminal state, then we should treat the obtained reward as the true return for that state - no bootstrapping.

MULTI-STEP A2C⌗

The A2C algorithm provides additional variance reduction by bootstrapping the estimate of the gradient, but it also adds bias to the estimate. While the vanilla policy gradient provides an un-biased estimator for the gradient, the A2C is not.

With the current setup there is one very obvious improvement that we could do to reduce the bias of the gradient estimate. Note that we are rolling out the policy for multiple steps, but we are computing the return using a single-step bootstrap. What we could do instead is use an n-step bootstrap estimation: the return for each state will be computed by summing all the rewards along the current trajectory and only at the end we will bootstrap:

$R (s_{t}, a_{t}) = r_{t + 1} + r_{t + 2} + \dots + r_{T} + V (s_{T}) .$

class A2CAgent(Agent):
    def update(self, states, actions, rewards, next_states, done):
        values = self.value(states)
        next_values = self.value(next_states)
        next_values = np.where(done, 0., next_values)

        N, T = rewards.shape
        returns = np.zeros_like(rewards)
        returns[:, -1] = np.where(done[:, -1], rewards[:, -1], values[:, -1])
        for t in range(T-2, -1, -1):
            returns[:, t] = rewards[:, t] + returns[:, t+1] * ~done[:, t]
        adv = returns - values

        # Update the policy network.
        adv = (adv - adv.mean()) / (adv.std() + 1e-8)
        logits = self.policy_network(states)
        logp = F.cross_entropy(logits, actions, reduction="none")
        loss = (logp * adv).mean()

        self.policy_optim.zero_grad()
        loss.backward()
        self.policy_optim.step()

        # Update the value network.
        for o, r in DataLoader((states, returns), self.batch_size):
            pred = self.value_network()
            vf_loss = F.mse_loss(pred, r)
            self.value_optim.zero_grad()
            vf_loss.backward()
            self.value_optim.step()

The computation of the returns might seem a bit confusing at first, because it runs the loop in reverse, starting from the end. We only need to bootstrap at the end for non-terminal states, and then we just keep adding the rewards from the previous steps to the current estimate of the return. If at some point we reach a terminal state, then the return is simply the obtained reward - no bootstrapping.

GAE⌗

Note that estimating the return using an n-step bootstrap reduces the bias, but increases the variance of the estimate. There is an obvious trade-off between bias and variance when choosing how large $n$ should be, i.e. how many steps to perform in the agent-environment loop.

An effective approach to reduce the variance and perfectly balance the trade-off is the Generalized Advantage Estimation(GAE)³. The formula is very similar to $T D (λ)$ where we have a weighted summation of different n-step returns. The $T D (λ)$ -return is given by:

$\begin{aligned} R^{λ} (s_{t}, a_{t}) & = (1 - λ) (r_{t + 1} + V (s_{t + 1})) + \\ + (1 - λ) λ (r_{t + 1} + r_{t + 2} + V (s_{t + 2})) + \\ + \dots + \\ + (1 - λ) λ^{T - t - 2} (r_{t + 1} + r_{t + 2} + \dots + r_{T - 1} + V (s_{T - 1})) + \\ + λ^{T - t - 1} (r_{t + 1} + r_{t + 2} + \dots + r_{T} + V (s_{T})) . \end{aligned}$

Here $λ$ is a parameter that controls the exponential weighting of the different n-step returns. For $λ = 0$ we get the simple one-step return used in online A2C, and for $λ = 1$ we get the full n-step return used in the multi-step A2C.

To get the generalized advantage estimator we simply subtract the value of $s_{t}$ : $A^{G A E} (s_{t}, a_{t}) = R^{λ} (s_{t}, a_{t}) - V (s_{t}) .$

Let us define $δ_{t} = r_{t + 1} + V (s_{t + 1}) - V (s_{t})$ . Working with advantages instead of returns will actually allow us to nicely simplify the formula using telescoping sums. For example the two-step advantage can be written as: $\begin{aligned} A_{t}^{(2)} & = r_{t + 1} + r_{t + 2} + V (s_{t + 2}) - V (s_{t}) \\ = r_{t + 1} + V (s_{t + 1}) - V (s_{t}) + r_{t + 2} + V (s_{t + 2}) - V (s_{t + 1}) \\ = δ_{t} + δ_{t + 1} . \end{aligned}$

Finally, for the GAE we get: $A^{G A E} = \sum_{i = 0}^{T - t - 1} λ^{i} δ_{t + i} .$

Again, I am glossing over the details of how this formula is derived, and why it works, but if you want to read more I suggest checking ot this blog post.

To use GAE in our A2C agent we simply have to modify the way we estimate the advantages:

class A2CAgent(Agent):
    def update(self, states, actions, rewards, next_states, done):
        values = self.value(states)
        next_values = self.value(next_states)
        next_values = np.where(done, 0., next_values)

        N, T = rewards.shape
        adv = np.zeros_like(rewards)
        adv[:, -1] = np.where(done[:, -1], rewards[:, -1] - values[:, -1], 0.)
        for t in range(T-2, -1, -1):
            delta = rewards[:, t] + values[:, t+1] * ~done[:, t] - values[:, t]
            adv[:, t] = delta + self.lamb * adv[:, t+1] * ~done[:, t]
        returns = adv + values

        # Update the policy network.
        adv = (adv - adv.mean()) / (adv.std() + 1e-8)
        logits = self.policy_network(states)
        logp = F.cross_entropy(logits, actions, reduction="none")
        loss = (logp * adv).mean()

        self.policy_optim.zero_grad()
        loss.backward()
        self.policy_optim.step()

        # Update the value network.
        for o, r in DataLoader((states, returns), self.batch_size):
            pred = self.value_network()
            vf_loss = F.mse_loss(pred, r)
            self.value_optim.zero_grad()
            vf_loss.backward()
            self.value_optim.step()

Again, the computation of the advantages might seem a bit confusing, but it follows a simple logic:

bootstrap at the end for non-terminal states,
at each step compute the current $δ_{t}$ by making sure not to bootstrap on terminal states,
compute the advantage by adding the $δ_{t}$ to the running sum of deltas decayed by $λ$ . Again make sure not to add anything if at a terminal state.

Since we are directly computing the advantages, the returns are actually derived by reversing the equation: $R^{λ} (s_{t}, a_{t}) = A^{G A E} (s_{t}, a_{t}) + V (s_{t})$ .

PPO⌗

Note that all of the policy updates that we did until now are simple “vanilla” updates: we simply update the policy once, using the estimate of the gradient, and then we discard the collected data. However, there is nothing stopping us from using a more sophisticated update algorithm. In fact, for the experiments in the GAE paper the authors use the TRPO⁴ update rule.

(Not really shocking news, given that both TRPO and GAE are authored by the same people.)

Here we will take a look at PPO⁵. The problem that the authors are trying to solve is to come up with an algorithm that allows us to take the biggest possible update step on the policy parameters before throwing out the collected rollout data. The approach taken in this paper is to allow for multiple update steps that, when combined, would approximate this maximum possible update.

Note that after we update the policy parameters once, $π_{θ} = π_{θ_{o l d}} + \nabla θ_{o l d}$ , every other update would actually be using off-policy data. To correct for this data miss-match we can use importance sampling:

$\begin{aligned} J (θ) & = E_{s_{t} \sim μ_{θ}, a_{t} \sim π_{θ}} [\sum_{t = 1} r_{t + 1}] \\ = E_{s_{t} \sim μ_{θ}, a_{t} \sim π_{θ}} [\frac{π_{θ_{o l d}} (a | s)}{π_{θ_{o l d}} (a | s)} \sum_{t = 1} r_{t + 1}] \\ = E_{s_{t} \sim μ_{θ}, a_{t} \sim π_{θ_{o l d}}} [\frac{π_{θ_{o l d}} (a | s)}{π_{θ} (a | s)} \sum_{t = 1} r_{t + 1}] \end{aligned}$

It seems like we could compute the objective to update the new policy weights using the data collected with the old policy weights, as long as we correct with the importance sampling weight:

$ρ (θ) = \frac{π_{θ} (a | s)}{π_{θ_{o l d}} (a | s)} .$

However, note that, in order to compute the correct gradient estimate, the actions have to be sampled under $π_{θ_{o l d}}$ , but the states have to be sampled under $μ_{θ}$ . Unfortunately our data was sampled under $μ_{θ_{o l d}}$ .

How bad is that?

It turns out that if $π_{θ}$ does not deviate too much from $π_{θ_{o l d}}$ , then using the old data sampled from $μ_{θ_{o l d}}$ is actually ok. The difference between the objectives calculated using $μ_{θ}$ and $μ_{θ_{o l d}}$ is bounded, and thus, optimizing one would also optimize the other. In simple words, it is ok to optimize the objective with the old data as long as $π_{θ}$ is close to $π_{θ_{o l d}}$ . A proof of this claim can be found in Appendix A of the TRPO paper⁴.

There are two different proximal policy algorithms each using a different heuristic to try to ensure that $π_{θ}$ is close to $π_{θ_{o l d}}$ :

PPO-Penalty - constraints the KL divergence between the two distributions by adding it as a penalty to the objective: $J (θ) = E_{s_{t} \sim μ_{θ}, a_{t} \sim π_{θ_{o l d}}} [ρ (θ) A (s, a) - β K L (π_{θ} (\cdot | s), π_{θ_{o l d}} (\cdot | s))]$
PPO-CLIP - clips the objective function if $π_{θ}$ deviates too much from $π_{θ_{o l d}}$ : $J (θ) = E_{s_{t} \sim μ_{θ}, a_{t} \sim π_{θ_{o l d}}} [min (ρ (θ) A (s, a), clip (ρ (θ), 1 - ϵ, 1 + ϵ) A (s, a))]$

The algorithm implemented here is PPO-CLIP augmented with a check for early stopping. We will split the collected rollout data into mini-batches and we will iterate for several epochs over the batches. At the end of every epoch we will check the KL divergence between the original policy $π_{θ_{o l d}}$ and the newest policy $π_{θ}$ . If a given threshold is reached, then we will stop updating and collect new rollout data.

class PPOAgent(Agent):
    def update(self, states, actions, rewards, next_states, done):
        values = self.value(states)
        next_values = self.value(next_states)
        next_values = np.where(done, 0., next_values)

        N, T = rewards.shape
        adv = np.zeros_like(rewards)
        adv[:, -1] = np.where(done[:, -1], rewards[:, -1] - values[:, -1], 0.)
        for t in range(T-2, -1, -1):
            delta = rewards[:, t] + values[:, t+1] * ~done[:, t] - values[:, t]
            adv[:, t] = delta + self.lamb * adv[:, t+1] * ~done[:, t]
        returns = adv + values

        # Update.
        logp_old = self.policy(states).log_prob(actions)
        values_old = self.value(states)
        for _ in range(self.n_epochs):
            dataset = DataLoader(
                (states, actions, returns, adv, logp_old, values_old),
                self.batch_size,
            )
            for s, a, r, ad, lp_old, v_old in dataset:
                # Update the policy network.
                logits = self.policy_network(s)
                logp = -F.cross_entropy(logits, a, reduction="none")
                rho = (logp - lp_old).exp()
                ad = (ad - ad.mean()) / (ad.std() + 1e-8)
                clip_adv = np.clip(rho, 1-self.pi_clip, 1+self.pi_clip) * ad
                pi_loss = (min(rho * ad, clip_adv)).mean()
                self.policy_optim.zero_grad()
                pi_loss.backward()
                self.policy_optim.step()

                # Update the value network.
                v_pred = self.value_network(s)
                v_clip = v_old + np.clip(v_pred-v_old, -self.vf_clip, self.vf_clip)
                vf_loss = (max((v_pred - r)**2, (v_clip - r)**2)).mean()
                self.value_optim.zero_grad()
                vf_loss.backward()
                self.value_optim.step()

            # Check for early stopping.
            logp = self.policy(states).log_prob(actions)
            KL = logp_old - logp
            if KL.mean() > 1.5 * self.tgt_KL:
                break

Note that the advantages are normalized at the mini-batch level, in the beginning of every iteration before computing the loss. This should come as no surprise, because as we mentioned earlier, the goal of this normalization is to stabilize the gradient updates of the neural network. It has nothing to do with the baseline for the return.

In addition to clipping the objective for the policy, we are also clipping the value function loss before updating the parameters of the value network. This approach was first introduced in the GAE paper³ (see Section 5), and was later also applied in the official PPO implementation.

Finally, to compute the KL divergence between the new and the old policy we use the following equality: $D_{K L} (P | | Q) = \sum_{x} P (x) \log \frac{P (x)}{Q (x)} = E_{x \sim P} [\log P (x) - \log Q (x)] .$ Checkout this blog post by John Schulman where he proposes a different unbiased estimator for the KL divergence that has less variance.

2013 “The optimal reward baseline for gradient-based reinforcement learning” by Lex Weaver, Nigel Tao ↩︎
2016 “Asynchronous methods for deep reinforcement learning” by Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu ↩︎
2015 “High-dimensional continuous control using generalized advantage estimation” by John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel ↩︎ ↩︎
2015 “Trust Region Policy Optimization” by John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel ↩︎ ↩︎
2017 “Proximal Policy Optimization Algorithms” by John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov ↩︎