refactored training approaches

engine/jax/primitives.py

@@ -308,6 +308,8 @@ if JAX_AVAILABLE:
         n_states: int,
     ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array]:
         k_actor, k_product, k_step = jax.random.split(key, 3)
+        start_idx_i32 = jnp.asarray(start_idx, dtype=jnp.int32)
+        term_idx_i32 = jnp.asarray(term_idx, dtype=jnp.int32)
         actor_draw = jax.random.uniform(k_actor, (n_sessions,))
         actors = (actor_draw < alpha).astype(jnp.int32)
         products = jax.random.randint(
@@ -315,7 +317,7 @@ if JAX_AVAILABLE:
         )
 
         active_init = jnp.ones((n_sessions,), dtype=jnp.bool_)
-        state_init = jnp.full((n_sessions,), int(start_idx), dtype=jnp.int32)
+        state_init = jnp.full((n_sessions,), start_idx_i32, dtype=jnp.int32)
 
         def _scan_step(carry, _):
             states, active, rng = carry
@@ -324,11 +326,11 @@ if JAX_AVAILABLE:
             probs_a = agent_T[states]
             probs = jnp.where(actors[:, None] == 0, probs_h, probs_a)
             next_state = jax.random.categorical(k, jnp.log(probs + 1e-10), axis=-1)
-            next_state = jnp.where(active, next_state, int(term_idx))
+            next_state = jnp.where(active, next_state, term_idx_i32)
             emitted = jnp.where(active, next_state, -1)
             is_terminal = terminal_mask[jnp.clip(next_state, 0, n_states - 1)]
             next_active = active & (~is_terminal)
-            carry_states = jnp.where(next_active, next_state, int(term_idx))
+            carry_states = jnp.where(next_active, next_state, term_idx_i32)
             return (carry_states, next_active, rng), emitted
 
         _, state_t = jax.lax.scan(