Generalized advantage estimation · Ingénierie IA

An actor critic needs an advantage for each step: how much better the action was than the critic expected. Generalized advantage estimation (GAE) mixes the one-step TD error with longer returns. The parameter \lambda controls the mix. A2C and PPO use GAE on every rollout.

Task

Write two functions.

td_errors(rewards, values, dones, gamma) returns the TD error of each step: \delta_t = r_t + \gamma (1 - d_t) V_{t+1} - V_t.
gae(rewards, values, dones, gamma, lam) returns a tuple (advantages, returns).

The advantages follow the recursion from the last step to the first step:

A_{T-1} = \delta_{T-1} and A_t = \delta_t + \gamma \lambda (1 - d_t) A_{t+1} for t < T - 1.

The returns are the targets for the critic: R_t = A_t + V_t.

Here d_t is 1 when dones[t] is True and 0 otherwise. A done flag at step t means that step t is the last step of its episode. The next value and the next advantage are then 0.

The array values has T + 1 entries. values[t] is the critic value of the state at step t. values[T] is the bootstrap value: the critic value of the state after the last step. It is used only when the last step has no done flag.

Input and output

Name	Type	Shape	Meaning
`rewards`	float array	`(T,)`	the reward at each step
`values`	float array	`(T + 1,)`	the critic value of each state, then the bootstrap value
`dones`	bool array	`(T,)`	`True` at the last step of each episode
`gamma`	float	scalar	the discount factor, from 0 to 1
`lam`	float	scalar	the GAE parameter \lambda, from 0 to 1

td_errors returns a float array of shape (T,).
gae returns a tuple of two float arrays, each of shape (T,): the advantages, then the returns.

Constraints

1 <= T <= 1000.
Do not normalize the advantages. Normalization is a separate step.
Do not change the input arrays.
Use numpy only.

Examples

python

rewards = np.array([1.0, 0.0, 1.0])
values = np.array([0.5, 0.4, 0.3, 0.2])
dones = np.array([False, False, True])
td_errors(rewards, values, dones, 0.9)

text

[ 0.86 -0.13  0.7 ]

python

gae(rewards, values, dones, 0.9, 0.8)

text

(array([1.12928, 0.374  , 0.7    ]), array([1.62928, 0.774  , 1.     ]))

python

rewards = np.array([1.0, 1.0, 1.0, 1.0])
values = np.array([1.0, 2.0, 1.0, 2.0, 3.0])
dones = np.array([False, True, False, False])
gae(rewards, values, dones, 0.5, 0.5)

text

(array([ 0.75 , -1.   ,  1.125,  0.5  ]), array([1.75 , 1.   , 2.125, 2.5  ]))