Expand Tests

test/test_ppo.py

@@ -3,12 +3,15 @@ import numpy as np
 from fancy_rl import PPO
 import gymnasium as gym
 
-@pytest.fixture
 def simple_env():
-    return gym.make('CartPole-v1')
+    return gym.make('LunarLander-v2', continuous=True)
 
 def test_ppo_instantiation():
-    ppo = PPO("CartPole-v1")
+    ppo = PPO(simple_env)
+    assert isinstance(ppo, PPO)
+
+def test_ppo_instantiation_from_str():
+    ppo = PPO('CartPole-v1')
     assert isinstance(ppo, PPO)
 
 @pytest.mark.parametrize("learning_rate", [1e-4, 3e-4, 1e-3])
@@ -19,7 +22,7 @@ def test_ppo_instantiation():
 @pytest.mark.parametrize("clip_range", [0.1, 0.2, 0.3])
 def test_ppo_initialization_with_different_hps(learning_rate, n_steps, batch_size, n_epochs, gamma, clip_range):
     ppo = PPO(
-        "CartPole-v1",
+        simple_env,
         learning_rate=learning_rate,
         n_steps=n_steps,
         batch_size=batch_size,
@@ -34,26 +37,42 @@ def test_ppo_initialization_with_different_hps(learning_rate, n_steps, batch_siz
     assert ppo.gamma == gamma
     assert ppo.clip_range == clip_range
 
-def test_ppo_predict(simple_env):
-    ppo = PPO("CartPole-v1")
-    obs, _ = simple_env.reset()
+def test_ppo_predict():
+    ppo = PPO(simple_env)
+    env = ppo.make_env()
+    obs, _ = env.reset()
     action, _ = ppo.predict(obs)
     assert isinstance(action, np.ndarray)
-    assert action.shape == simple_env.action_space.shape
+    assert action.shape == env.action_space.shape
 
 def test_ppo_learn():
-    ppo = PPO("CartPole-v1", n_steps=64, batch_size=32)
-    env = gym.make("CartPole-v1")
+    ppo = PPO(simple_env, n_steps=64, batch_size=32)
+    env = ppo.make_env()
     obs, _ = env.reset()
     for _ in range(64):
         action, _ = ppo.predict(obs)
-        next_obs, reward, done, truncated, _ = env.step(action)
-        ppo.store_transition(obs, action, reward, done, next_obs)
-        obs = next_obs
+        obs, reward, done, truncated, _ = env.step(action)
         if done or truncated:
             obs, _ = env.reset()
 
-    loss = ppo.learn()
-    assert isinstance(loss, dict)
-    assert "policy_loss" in loss
-    assert "value_loss" in loss
+def test_ppo_training():
+    ppo = PPO(simple_env, total_timesteps=10000)
+    env = ppo.make_env()
+    
+    initial_performance = evaluate_policy(ppo, env)
+    ppo.train()
+    final_performance = evaluate_policy(ppo, env)
+    
+    assert final_performance > initial_performance, "PPO should improve performance after training"
+
+def evaluate_policy(policy, env, n_eval_episodes=10):
+    total_reward = 0
+    for _ in range(n_eval_episodes):
+        obs, _ = env.reset()
+        done = False
+        while not done:
+            action, _ = policy.predict(obs)
+            obs, reward, terminated, truncated, _ = env.step(action)
+            total_reward += reward
+            done = terminated or truncated
+    return total_reward / n_eval_episodes

test/test_trpl.py

@@ -3,12 +3,15 @@ import numpy as np
 from fancy_rl import TRPL
 import gymnasium as gym
 
-@pytest.fixture
 def simple_env():
-    return gym.make('CartPole-v1')
+    return gym.make('LunarLander-v2', continuous=True)
 
 def test_trpl_instantiation():
-    trpl = TRPL("CartPole-v1")
+    trpl = TRPL(simple_env)
+    assert isinstance(trpl, TRPL)
+
+def test_trpl_instantiation_from_str():
+    trpl = TRPL('MountainCarContinuous-v0')
     assert isinstance(trpl, TRPL)
 
 @pytest.mark.parametrize("learning_rate", [1e-4, 3e-4, 1e-3])
@@ -19,7 +22,7 @@ def test_trpl_instantiation():
 @pytest.mark.parametrize("trust_region_bound_cov", [0.0005, 0.001])
 def test_trpl_initialization_with_different_hps(learning_rate, n_steps, batch_size, gamma, trust_region_bound_mean, trust_region_bound_cov):
     trpl = TRPL(
-        "CartPole-v1",
+        simple_env,
         learning_rate=learning_rate,
         n_steps=n_steps,
         batch_size=batch_size,
@@ -34,16 +37,17 @@ def test_trpl_initialization_with_different_hps(learning_rate, n_steps, batch_si
     assert trpl.projection.trust_region_bound_mean == trust_region_bound_mean
     assert trpl.projection.trust_region_bound_cov == trust_region_bound_cov
 
-def test_trpl_predict(simple_env):
-    trpl = TRPL("CartPole-v1")
-    obs, _ = simple_env.reset()
+def test_trpl_predict():
+    trpl = TRPL(simple_env)
+    env = trpl.make_env()
+    obs, _ = env.reset()
     action, _ = trpl.predict(obs)
     assert isinstance(action, np.ndarray)
-    assert action.shape == simple_env.action_space.shape
+    assert action.shape == env.action_space.shape
 
 def test_trpl_learn():
-    trpl = TRPL("CartPole-v1", n_steps=64, batch_size=32)
-    env = gym.make("CartPole-v1")
+    trpl = TRPL(simple_env, n_steps=64, batch_size=32)
+    env = trpl.make_env()
     obs, _ = env.reset()
     for _ in range(64):
         action, _ = trpl.predict(obs)
@@ -58,12 +62,13 @@ def test_trpl_learn():
     assert "policy_loss" in loss
     assert "value_loss" in loss
 
-def test_trpl_training(simple_env):
-    trpl = TRPL("CartPole-v1", total_timesteps=10000)
+def test_trpl_training():
+    trpl = TRPL(simple_env, total_timesteps=10000)
+    env = trpl.make_env()
     
-    initial_performance = evaluate_policy(trpl, simple_env)
+    initial_performance = evaluate_policy(trpl, env)
     trpl.train()
-    final_performance = evaluate_policy(trpl, simple_env)
+    final_performance = evaluate_policy(trpl, env)
     
     assert final_performance > initial_performance, "TRPL should improve performance after training"