77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import pytest
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import numpy as np
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from fancy_rl import TRPL
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import gymnasium as gym
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@pytest.fixture
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def simple_env():
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return gym.make('CartPole-v1')
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def test_trpl_instantiation():
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trpl = TRPL("CartPole-v1")
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assert isinstance(trpl, TRPL)
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@pytest.mark.parametrize("learning_rate", [1e-4, 3e-4, 1e-3])
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@pytest.mark.parametrize("n_steps", [1024, 2048])
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@pytest.mark.parametrize("batch_size", [32, 64, 128])
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@pytest.mark.parametrize("gamma", [0.95, 0.99])
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@pytest.mark.parametrize("max_kl", [0.01, 0.05])
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def test_trpl_initialization_with_different_hps(learning_rate, n_steps, batch_size, gamma, max_kl):
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trpl = TRPL(
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"CartPole-v1",
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learning_rate=learning_rate,
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n_steps=n_steps,
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batch_size=batch_size,
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gamma=gamma,
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max_kl=max_kl
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)
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assert trpl.learning_rate == learning_rate
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assert trpl.n_steps == n_steps
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assert trpl.batch_size == batch_size
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assert trpl.gamma == gamma
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assert trpl.max_kl == max_kl
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def test_trpl_predict(simple_env):
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trpl = TRPL("CartPole-v1")
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obs, _ = simple_env.reset()
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action, _ = trpl.predict(obs)
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assert isinstance(action, np.ndarray)
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assert action.shape == simple_env.action_space.shape
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def test_trpl_learn():
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trpl = TRPL("CartPole-v1", n_steps=64, batch_size=32)
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env = gym.make("CartPole-v1")
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obs, _ = env.reset()
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for _ in range(64):
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action, _ = trpl.predict(obs)
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next_obs, reward, done, truncated, _ = env.step(action)
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trpl.store_transition(obs, action, reward, done, next_obs)
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obs = next_obs
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if done or truncated:
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obs, _ = env.reset()
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loss = trpl.learn()
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assert isinstance(loss, dict)
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assert "policy_loss" in loss
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assert "value_loss" in loss
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def test_trpl_training(simple_env):
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trpl = TRPL("CartPole-v1", total_timesteps=10000)
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initial_performance = evaluate_policy(trpl, simple_env)
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trpl.train()
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final_performance = evaluate_policy(trpl, simple_env)
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assert final_performance > initial_performance, "TRPL should improve performance after training"
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def evaluate_policy(policy, env, n_eval_episodes=10):
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total_reward = 0
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for _ in range(n_eval_episodes):
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obs, _ = env.reset()
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done = False
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while not done:
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action, _ = policy.predict(obs)
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obs, reward, terminated, truncated, _ = env.step(action)
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total_reward += reward
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done = terminated or truncated
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return total_reward / n_eval_episodes |