DCG-MAP-Elites

examples/distributed_mapelites.ipynb

@@ -348,7 +348,7 @@
     "repertoire, emitter_state, random_key = map_elites.get_distributed_init_fn(\n",
     "    centroids=centroids,\n",
     "    devices=devices,\n",
-    ")(init_genotypes=init_variables, random_key=random_key)"
+    ")(genotypes=init_variables, random_key=random_key)"
    ]
   },
   {

examples/me_sac_pbt.ipynb

@@ -311,7 +311,7 @@
     "# initialize map-elites\n",
     "repertoire, emitter_state, keys = map_elites.get_distributed_init_fn(\n",
     "    devices=devices, centroids=centroids\n",
-    ")(init_genotypes=training_states, random_key=keys)"
+    ")(genotypes=training_states, random_key=keys)"
    ]
   },
   {

examples/me_td3_pbt.ipynb

@@ -314,7 +314,7 @@
     "# initialize map-elites\n",
     "repertoire, emitter_state, keys = map_elites.get_distributed_init_fn(\n",
     "    devices=devices, centroids=centroids\n",
-    ")(init_genotypes=training_states, random_key=keys)"
+    ")(genotypes=training_states, random_key=keys)"
    ]
   },
   {

examples/mome.ipynb

@@ -212,7 +212,7 @@
     "# initial population\n",
     "random_key = jax.random.PRNGKey(42)\n",
     "random_key, subkey = jax.random.split(random_key)\n",
-    "init_genotypes = jax.random.uniform(\n",
+    "genotypes = jax.random.uniform(\n",
     "    random_key, (batch_size, num_variables), minval=minval, maxval=maxval, dtype=jnp.float32\n",
     ")\n",
     "\n",
@@ -303,7 +303,7 @@
    "outputs": [],
    "source": [
     "repertoire, emitter_state, random_key = mome.init(\n",
-    "    init_genotypes,\n",
+    "    genotypes,\n",
     "    centroids,\n",
     "    pareto_front_max_length,\n",
     "    random_key\n",

examples/nsga2_spea2.ipynb

@@ -189,7 +189,7 @@
     "# Initial population\n",
     "random_key = jax.random.PRNGKey(0)\n",
     "random_key, subkey = jax.random.split(random_key)\n",
-    "init_genotypes = jax.random.uniform(\n",
+    "genotypes = jax.random.uniform(\n",
     "    subkey, (batch_size, genotype_dim), minval=minval, maxval=maxval, dtype=jnp.float32\n",
     ")\n",
     "\n",
@@ -238,7 +238,7 @@
     "\n",
     "# init nsga2\n",
     "repertoire, emitter_state, random_key = nsga2.init(\n",
-    "    init_genotypes,\n",
+    "    genotypes,\n",
     "    population_size,\n",
     "    random_key\n",
     ")"
@@ -303,7 +303,7 @@
     "\n",
     "# init spea2\n",
     "repertoire, emitter_state, random_key = spea2.init(\n",
-    "    init_genotypes,\n",
+    "    genotypes,\n",
     "    population_size,\n",
     "    num_neighbours,\n",
     "    random_key\n",

qdax/baselines/genetic_algorithm.py

@@ -39,12 +39,12 @@ def __init__(
 
     @partial(jax.jit, static_argnames=("self", "population_size"))
     def init(
-        self, init_genotypes: Genotype, population_size: int, random_key: RNGKey
+        self, genotypes: Genotype, population_size: int, random_key: RNGKey
     ) -> Tuple[GARepertoire, Optional[EmitterState], RNGKey]:
         """Initialize a GARepertoire with an initial population of genotypes.
 
         Args:
-            init_genotypes: the initial population of genotypes
+            genotypes: the initial population of genotypes
             population_size: the maximal size of the repertoire
             random_key: a random key to handle stochastic operations
 
@@ -54,26 +54,21 @@ def init(
 
         # score initial genotypes
         fitnesses, extra_scores, random_key = self._scoring_function(
-            init_genotypes, random_key
+            genotypes, random_key
         )
 
         # init the repertoire
         repertoire = GARepertoire.init(
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             population_size=population_size,
         )
 
         # get initial state of the emitter
         emitter_state, random_key = self._emitter.init(
-            init_genotypes=init_genotypes, random_key=random_key
-        )
-
-        # update emitter state
-        emitter_state = self._emitter.state_update(
-            emitter_state=emitter_state,
+            random_key=random_key,
             repertoire=repertoire,
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             descriptors=None,
             extra_scores=extra_scores,
@@ -108,7 +103,7 @@ def update(
         """
 
         # generate offsprings
-        genotypes, random_key = self._emitter.emit(
+        genotypes, extra_info, random_key = self._emitter.emit(
             repertoire, emitter_state, random_key
         )
 
@@ -127,7 +122,7 @@ def update(
             genotypes=genotypes,
             fitnesses=fitnesses,
             descriptors=None,
-            extra_scores=extra_scores,
+            extra_scores={**extra_scores, **extra_info},
         )
 
         # update the metrics

qdax/baselines/nsga2.py

@@ -28,31 +28,36 @@ class NSGA2(GeneticAlgorithm):
 
     @partial(jax.jit, static_argnames=("self", "population_size"))
     def init(
-        self, init_genotypes: Genotype, population_size: int, random_key: RNGKey
+        self, genotypes: Genotype, population_size: int, random_key: RNGKey
     ) -> Tuple[NSGA2Repertoire, Optional[EmitterState], RNGKey]:
 
         # score initial genotypes
         fitnesses, extra_scores, random_key = self._scoring_function(
-            init_genotypes, random_key
+            genotypes, random_key
         )
 
         # init the repertoire
         repertoire = NSGA2Repertoire.init(
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             population_size=population_size,
         )
 
         # get initial state of the emitter
         emitter_state, random_key = self._emitter.init(
-            init_genotypes=init_genotypes, random_key=random_key
+            random_key=random_key,
+            repertoire=repertoire,
+            genotypes=genotypes,
+            fitnesses=fitnesses,
+            descriptors=None,
+            extra_scores=extra_scores,
         )
 
         # update emitter state
         emitter_state = self._emitter.state_update(
             emitter_state=emitter_state,
             repertoire=repertoire,
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             extra_scores=extra_scores,
         )

qdax/baselines/spea2.py

@@ -40,35 +40,40 @@ class SPEA2(GeneticAlgorithm):
     )
     def init(
         self,
-        init_genotypes: Genotype,
+        genotypes: Genotype,
         population_size: int,
         num_neighbours: int,
         random_key: RNGKey,
     ) -> Tuple[SPEA2Repertoire, Optional[EmitterState], RNGKey]:
 
         # score initial genotypes
         fitnesses, extra_scores, random_key = self._scoring_function(
-            init_genotypes, random_key
+            genotypes, random_key
         )
 
         # init the repertoire
         repertoire = SPEA2Repertoire.init(
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             population_size=population_size,
             num_neighbours=num_neighbours,
         )
 
         # get initial state of the emitter
         emitter_state, random_key = self._emitter.init(
-            init_genotypes=init_genotypes, random_key=random_key
+            random_key=random_key,
+            repertoire=repertoire,
+            genotypes=genotypes,
+            fitnesses=fitnesses,
+            descriptors=None,
+            extra_scores=extra_scores,
         )
 
         # update emitter state
         emitter_state = self._emitter.state_update(
             emitter_state=emitter_state,
             repertoire=repertoire,
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             extra_scores=extra_scores,
         )

qdax/core/aurora.py

@@ -118,7 +118,7 @@ def container_size_control(
 
     def init(
         self,
-        init_genotypes: Genotype,
+        genotypes: Genotype,
         aurora_extra_info: AuroraExtraInfo,
         l_value: jnp.ndarray,
         max_size: int,
@@ -128,7 +128,7 @@ def init(
         genotypes. Also performs the first training of the AURORA encoder.
 
         Args:
-            init_genotypes: initial genotypes, pytree in which leaves
+            genotypes: initial genotypes, pytree in which leaves
                 have shape (batch_size, num_features)
             aurora_extra_info: information to perform AURORA encodings,
                 such as the encoder parameters
@@ -141,7 +141,7 @@ def init(
             the emitter, and the updated information to perform AURORA encodings
         """
         fitnesses, descriptors, extra_scores, random_key = self._scoring_function(
-            init_genotypes,
+            genotypes,
             random_key,
         )
 
@@ -150,7 +150,7 @@ def init(
         descriptors = self._encoder_fn(observations, aurora_extra_info)
 
         repertoire = UnstructuredRepertoire.init(
-            genotypes=init_genotypes,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             descriptors=descriptors,
             observations=observations,
@@ -160,13 +160,9 @@ def init(
 
         # get initial state of the emitter
         emitter_state, random_key = self._emitter.init(
-            init_genotypes=init_genotypes, random_key=random_key
-        )
-
-        # update emitter state
-        emitter_state = self._emitter.state_update(
-            emitter_state=emitter_state,
-            genotypes=init_genotypes,
+            random_key=random_key,
+            repertoire=repertoire,
+            genotypes=genotypes,
             fitnesses=fitnesses,
             descriptors=descriptors,
             extra_scores=extra_scores,
@@ -208,9 +204,10 @@ def update(
             a new key
         """
         # generate offsprings with the emitter
-        genotypes, random_key = self._emitter.emit(
+        genotypes, extra_info, random_key = self._emitter.emit(
             repertoire, emitter_state, random_key
         )
+
         # scores the offsprings
         fitnesses, descriptors, extra_scores, random_key = self._scoring_function(
             genotypes,
@@ -232,10 +229,11 @@ def update(
         # update emitter state after scoring is made
         emitter_state = self._emitter.state_update(
             emitter_state=emitter_state,
+            repertoire=repertoire,
             genotypes=genotypes,
             fitnesses=fitnesses,
             descriptors=descriptors,
-            extra_scores=extra_scores,
+            extra_scores=extra_scores | extra_info,
         )
 
         # update the metrics

qdax/core/containers/mapelites_repertoire.py

@@ -228,6 +228,38 @@ def sample(self, random_key: RNGKey, num_samples: int) -> Tuple[Genotype, RNGKey
 
         return samples, random_key
 
+    @partial(jax.jit, static_argnames=("num_samples",))
+    def sample_with_descs(
+        self,
+        random_key: RNGKey,
+        num_samples: int,
+    ) -> Tuple[Genotype, Descriptor, RNGKey]:
+        """Sample elements in the repertoire.
+
+        Args:
+            random_key: a jax PRNG random key
+            num_samples: the number of elements to be sampled
+
+        Returns:
+            samples: a batch of genotypes sampled in the repertoire
+            random_key: an updated jax PRNG random key
+        """
+
+        repertoire_empty = self.fitnesses == -jnp.inf
+        p = (1.0 - repertoire_empty) / jnp.sum(1.0 - repertoire_empty)
+
+        random_key, subkey = jax.random.split(random_key)
+        samples = jax.tree_util.tree_map(
+            lambda x: jax.random.choice(subkey, x, shape=(num_samples,), p=p),
+            self.genotypes,
+        )
+        descs = jax.tree_util.tree_map(
+            lambda x: jax.random.choice(subkey, x, shape=(num_samples,), p=p),
+            self.descriptors,
+        )
+
+        return samples, descs, random_key
+
     @jax.jit
     def add(
         self,