fix: Change to new-style-jax-rng-keys.

README.md

@@ -64,7 +64,7 @@ min_bd = 0.0
 max_bd = 1.0
 
 # Init a random key
-random_key = jax.random.PRNGKey(seed)
+random_key = jax.random.key(seed)
 
 # Init population of controllers
 random_key, subkey = jax.random.split(random_key)

examples/aurora.ipynb

@@ -146,7 +146,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/cmaes.ipynb

@@ -178,7 +178,7 @@
    "outputs": [],
    "source": [
     "state = cmaes.init()\n",
-    "random_key = jax.random.PRNGKey(0)"
+    "random_key = jax.random.key(0)"
    ]
   },
   {

examples/cmame.ipynb

@@ -205,7 +205,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "random_key = jax.random.PRNGKey(0)\n",
+    "random_key = jax.random.key(0)\n",
     "# in CMA-ME settings (from the paper), there is no init population\n",
     "# we multipy by zero to reproduce this setting\n",
     "initial_population = jax.random.uniform(random_key, shape=(batch_size, num_dimensions)) * 0.\n",

examples/cmamega.ipynb

@@ -198,7 +198,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "random_key = jax.random.PRNGKey(0)\n",
+    "random_key = jax.key(0)\n",
     "# no initial population - give all the same value as emitter init value\n",
     "initial_population = jax.random.uniform(random_key, shape=(batch_size, num_dimensions)) * 0.\n",
     "\n",

examples/dads.ipynb

@@ -163,7 +163,7 @@
     "    eval_metrics=True,\n",
     ")\n",
     "\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "env_state = jax.jit(env.reset)(rng=key)\n",
     "eval_env_first_state = jax.jit(eval_env.reset)(rng=key)\n",
     "\n",
@@ -499,7 +499,7 @@
    "outputs": [],
    "source": [
     "rollout = []\n",
-    "random_key = jax.random.PRNGKey(seed=1)\n",
+    "random_key = jax.random.key(seed=1)\n",
     "state = jit_env_reset(rng=random_key)\n",
     "while not state.done:\n",
     "    rollout.append(state)\n",

examples/dcrlme.ipynb

@@ -154,7 +154,7 @@
    "source": [
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init environment\n",
     "env = environments.create(env_name, episode_length=episode_length)\n",

examples/diayn.ipynb

@@ -162,7 +162,7 @@
     "    eval_metrics=True,\n",
     ")\n",
     "\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "env_state = jax.jit(env.reset)(rng=key)\n",
     "eval_env_first_state = jax.jit(eval_env.reset)(rng=key)\n",
     "\n",
@@ -490,7 +490,7 @@
    "outputs": [],
    "source": [
     "rollout = []\n",
-    "random_key = jax.random.PRNGKey(seed=1)\n",
+    "random_key = jax.random.key(seed=1)\n",
     "state = jit_env_reset(rng=random_key)\n",
     "while not state.done:\n",
     "    rollout.append(state)\n",

examples/distributed_mapelites.ipynb

@@ -176,7 +176,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/jumanji_snake.ipynb

@@ -113,7 +113,7 @@
     "env = jumanji.make('Snake-v1')\n",
     "\n",
     "# Reset your (jit-able) environment\n",
-    "key = jax.random.PRNGKey(0)\n",
+    "key = jax.random.key(0)\n",
     "state, timestep = jax.jit(env.reset)(key)\n",
     "\n",
     "# Interact with the (jit-able) environment\n",
@@ -137,7 +137,7 @@
    "outputs": [],
    "source": [
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# get number of actions\n",
     "num_actions = env.action_spec().maximum + 1\n",

examples/mapelites.ipynb

@@ -133,7 +133,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.Key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",
@@ -494,7 +494,7 @@
    "outputs": [],
    "source": [
     "rollout = []\n",
-    "rng = jax.random.PRNGKey(seed=1)\n",
+    "rng = jax.random.key(seed=1)\n",
     "state = jit_env_reset(rng=rng)\n",
     "while not state.done:\n",
     "    rollout.append(state)\n",

examples/me_sac_pbt.ipynb

@@ -150,7 +150,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "key, subkey = jax.random.split(key)\n",
     "env_states = jax.jit(env.reset)(rng=subkey)\n",
     "eval_env_first_states = jax.jit(eval_env.reset)(rng=subkey)"
@@ -311,6 +311,10 @@
     "    observation_size=env.observation_size,\n",
     "    buffer_size=buffer_size,\n",
     ")\n",
+    "\n",
+    "# Need to convert to PRNGKey because of github.com/jax-ml/jax/issues/23647\n",
+    "keys = jax.random.key_data(keys)\n",
+    "\n",
     "keys, training_states, _ = jax.pmap(agent_init_fn, axis_name=\"p\", devices=devices)(keys)"
    ]
   },
@@ -504,7 +508,7 @@
     "%%time\n",
     "rollout = []\n",
     "\n",
-    "rng = jax.random.PRNGKey(seed=1)\n",
+    "rng = jax.random.key(seed=1)\n",
     "env_state = jax.jit(env.reset)(rng=rng)\n",
     "\n",
     "training_state, env_state = jax.tree_map(\n",

examples/me_td3_pbt.ipynb

@@ -155,7 +155,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "key, subkey = jax.random.split(key)\n",
     "env_states = jax.jit(env.reset)(rng=subkey)\n",
     "eval_env_first_states = jax.jit(eval_env.reset)(rng=subkey)"
@@ -314,6 +314,10 @@
     "    observation_size=env.observation_size,\n",
     "    buffer_size=buffer_size,\n",
     ")\n",
+    "\n",
+    "# Need to convert to PRNGKey because of github.com/jax-ml/jax/issues/23647\n",
+    "keys = jax.random.key_data(keys)\n",
+    "\n",
     "keys, training_states, _ = jax.pmap(agent_init_fn, axis_name=\"p\", devices=devices)(keys)"
    ]
   },

examples/mees.ipynb

@@ -151,7 +151,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/mels.ipynb

@@ -140,7 +140,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",
@@ -509,7 +509,7 @@
    "outputs": [],
    "source": [
     "rollout = []\n",
-    "rng = jax.random.PRNGKey(seed=1)\n",
+    "rng = jax.random.key(seed=1)\n",
     "state = jit_env_reset(rng=rng)\n",
     "while not state.done:\n",
     "    rollout.append(state)\n",

examples/mome.ipynb

@@ -205,7 +205,7 @@
    "outputs": [],
    "source": [
     "# initial population\n",
-    "random_key = jax.random.PRNGKey(42)\n",
+    "random_key = jax.random.key(42)\n",
     "random_key, subkey = jax.random.split(random_key)\n",
     "genotypes = jax.random.uniform(\n",
     "    random_key, (batch_size, num_variables), minval=minval, maxval=maxval, dtype=jnp.float32\n",

examples/nsga2_spea2.ipynb

@@ -181,7 +181,7 @@
    "outputs": [],
    "source": [
     "# Initial population\n",
-    "random_key = jax.random.PRNGKey(0)\n",
+    "random_key = jax.random.key(0)\n",
     "random_key, subkey = jax.random.split(random_key)\n",
     "genotypes = jax.random.uniform(\n",
     "    subkey, (batch_size, genotype_dim), minval=minval, maxval=maxval, dtype=jnp.float32\n",

examples/omgmega.ipynb

@@ -184,7 +184,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "random_key = jax.random.PRNGKey(0)\n",
+    "random_key = jax.random.key(0)\n",
     "\n",
     "# defines the population\n",
     "random_key, subkey = jax.random.split(random_key)\n",

examples/pga_aurora.ipynb

@@ -164,7 +164,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/pgame.ipynb

@@ -143,7 +143,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/qdpg.ipynb

@@ -157,7 +157,7 @@
     "env = environments.create(env_name, episode_length=episode_length)\n",
     "\n",
     "# Init a random key\n",
-    "random_key = jax.random.PRNGKey(seed)\n",
+    "random_key = jax.random.key(seed)\n",
     "\n",
     "# Init policy network\n",
     "policy_layer_sizes = policy_hidden_layer_sizes + (env.action_size,)\n",

examples/sac_pbt.ipynb

@@ -210,7 +210,7 @@
    "outputs": [],
    "source": [
     "# %%time\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "key, *keys = jax.random.split(key, num=1 + num_devices)\n",
     "keys = jnp.stack(keys)\n",
     "env_states, eval_env_first_states = jax.pmap(\n",
@@ -269,6 +269,10 @@
     "    observation_size=env.observation_size,\n",
     "    buffer_size=buffer_size,\n",
     ")\n",
+    "\n",
+    "# Need to convert to PRNGKey because of github.com/jax-ml/jax/issues/23647\n",
+    "keys = jax.random.key_data(keys)\n",
+    "\n",
     "keys, training_states, replay_buffers = jax.pmap(\n",
     "    agent_init_fn, axis_name=\"p\", devices=devices\n",
     ")(keys)"
@@ -518,7 +522,7 @@
     "%%time\n",
     "rollout = []\n",
     "\n",
-    "rng = jax.random.PRNGKey(seed=1)\n",
+    "rng = jax.random.key(seed=1)\n",
     "env_state = jax.jit(env.reset)(rng=rng)\n",
     "\n",
     "training_state, env_state = jax.tree_util.tree_map(\n",

examples/scripts/me_example.py

@@ -26,7 +26,7 @@ def run_me() -> None:
     max_bd = 1.0
 
     # Init a random key
-    random_key = jax.random.PRNGKey(seed)
+    random_key = jax.random.key(seed)
 
     # Init population of controllers
     random_key, subkey = jax.random.split(random_key)

examples/smerl.ipynb

@@ -168,7 +168,7 @@
     "    eval_metrics=True,\n",
     ")\n",
     "\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.Key(seed)\n",
     "env_state = jax.jit(env.reset)(rng=key)\n",
     "eval_env_first_state = jax.jit(eval_env.reset)(rng=key)\n",
     "\n",
@@ -504,7 +504,7 @@
    "outputs": [],
    "source": [
     "rollout = []\n",
-    "random_key = jax.random.PRNGKey(seed=1)\n",
+    "random_key = jax.random.key(seed=1)\n",
     "state = jit_env_reset(rng=random_key)\n",
     "while not state.done:\n",
     "    rollout.append(state)\n",

examples/td3_pbt.ipynb

@@ -181,7 +181,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "key = jax.random.PRNGKey(seed)\n",
+    "key = jax.random.key(seed)\n",
     "key, *keys = jax.random.split(key, num=1 + num_devices)\n",
     "keys = jnp.stack(keys)\n",
     "env_states, eval_env_first_states = jax.pmap(\n",
@@ -232,6 +232,10 @@
     "    observation_size=env.observation_size,\n",
     "    buffer_size=buffer_size,\n",
     ")\n",
+    "\n",
+    "# Need to convert to PRNGKey because of github.com/jax-ml/jax/issues/23647\n",
+    "keys = jax.random.key_data(keys)\n",
+    "\n",
     "keys, training_states, replay_buffers = jax.pmap(\n",
     "    agent_init_fn, axis_name=\"p\", devices=devices\n",
     ")(keys)"

qdax/core/containers/mapelites_repertoire.py

@@ -63,7 +63,7 @@ def compute_cvt_centroids(
         init="k-means++",
         n_clusters=num_centroids,
         n_init=1,
-        random_state=RandomState(subkey),
+        random_state=RandomState(jax.random.key_data(subkey)),
     )
     k_means.fit(x)
     centroids = k_means.cluster_centers_