Fix bug in toxin so that it has food_2 reward

oist · Nov 12, 2024 · cf653d0 · cf653d0
1 parent 5b80227
commit cf653d0
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Showing 2 changed files with 21 additions and 9 deletions.
diff --git a/experiments/cf_simple.py b/experiments/cf_simple.py
@@ -501,7 +501,12 @@ def replay(
     end: int | None = None,
     cfconfig_path: Path = DEFAULT_CFCONFIG,
     env_override: str = "",
+    scale: float = 1.0,
+    force_cpu: bool = False,
 ) -> None:
+    if force_cpu:
+        jax.config.update("jax_default_device", jax.devices("cpu")[0])
+
     with cfconfig_path.open("r") as f:
         cfconfig = toml.from_toml(CfConfig, f.read())
     # For speedup
@@ -513,14 +518,16 @@ def replay(
     end_index = end if end is not None else phys_state.circle_axy.shape[0]
     visualizer = env.visualizer(
         env_state,
-        figsize=(cfconfig.xlim[1] * 2, cfconfig.ylim[1] * 2),
+        figsize=(cfconfig.xlim[1] * scale, cfconfig.ylim[1] * scale),
         backend=backend,
     )
     if videopath is not None:
         visualizer = SaveVideoWrapper(visualizer, videopath, fps=60)
     for i in range(start, end_index):
-        phys = phys_state.set_by_index(i, env_state.physics)
-        env_state = dataclasses.replace(env_state, physics=phys)
+        ph = phys_state.set_by_index(i, env_state.physics)
+        # Disable rendering agents
+        ph = ph.nested_replace("circle.is_active", jnp.zeros_like(ph.circle.is_active))
+        env_state = dataclasses.replace(env_state, physics=ph)
         visualizer.render(env_state.physics)
         visualizer.show()
     visualizer.close()

diff --git a/experiments/cf_toxin.py b/experiments/cf_toxin.py
@@ -70,12 +70,14 @@ def normalize_action(self, action: jax.Array) -> jax.Array:
     def extract(
         self,
         ate_food: jax.Array,
+        ate_toxin: jax.Array,
         action: jax.Array,
-        energy: jax.Array,
     ) -> jax.Array:
-        del energy
         act_input = self.act_coef * self.normalize_action(action)
-        return jnp.concatenate((ate_food.astype(jnp.float32), act_input), axis=1)
+        return jnp.concatenate(
+            (ate_food.astype(jnp.float32), ate_toxin.astype(jnp.float32), act_input),
+            axis=1,
+        )
 
 
 def serialize_weight(w: jax.Array) -> dict[str, jax.Array]:
@@ -110,8 +112,11 @@ def step_rollout(
             env.act_space.sigmoid_scale(actions),  # type: ignore
         )
         obs_t1 = timestep.obs
-        energy = state_t.status.energy
-        rewards = reward_fn(timestep.info["n_ate_food"], actions, energy).reshape(-1, 1)
+        rewards = reward_fn(
+            timestep.info["n_ate_food"],
+            jnp.expand_dims(timestep.info["n_ate_toxin"], axis=1),
+            actions,
+        ).reshape(-1, 1)
         rollout = ppo.Rollout(
             observations=obs_t_array,
             actions=actions,
@@ -464,7 +469,7 @@ def evolve(
     reward_fn_instance = rfn.LinearReward(
         key=reward_key,
         n_agents=cfconfig.n_max_agents,
-        n_weights=cfconfig.n_food_sources,  # Because one of the foods is toxin
+        n_weights=cfconfig.n_food_sources + 1,
         std=gopsconfig.init_std,
         mean=gopsconfig.init_mean,
         extractor=reward_extracor.extract,