Try lower image resolution

scratch/etienne/intersimple/ppo_speed_image_lowres.py

@@ -0,0 +1,49 @@
+# %%
+from stable_baselines3 import PPO
+from intersimple.intersimple import NRasterized, speed_reward
+import functools
+
+model_name = "ppo_speed_image_lowres"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = NRasterized(
+    agent=51,
+    height=36,
+    width=36,
+    m_per_px=2,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)
+
+# %%

scratch/etienne/intersimple/ppo_speed_image_lowres_random.py

@@ -0,0 +1,42 @@
+# %%
+from stable_baselines3 import PPO
+from intersimple.intersimple import NRasterizedRandomAgent, speed_reward
+import functools
+
+model_name = "ppo_speed_image_lowres_random"
+
+env = NRasterizedRandomAgent(
+    height=36,
+    width=36,
+    m_per_px=2,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    )
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+    batch_size=2048,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)