81 lines
2.7 KiB
Plaintext
81 lines
2.7 KiB
Plaintext
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import pytest
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import torch
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import numpy as np
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from fancy_rl import VLEARN
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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_vlearn_instantiation():
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vlearn = VLEARN("CartPole-v1")
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assert isinstance(vlearn, VLEARN)
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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("mean_bound", [0.05, 0.1])
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@pytest.mark.parametrize("cov_bound", [0.0005, 0.001])
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def test_vlearn_initialization_with_different_hps(learning_rate, n_steps, batch_size, gamma, mean_bound, cov_bound):
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vlearn = VLEARN(
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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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mean_bound=mean_bound,
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cov_bound=cov_bound
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)
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assert vlearn.learning_rate == learning_rate
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assert vlearn.n_steps == n_steps
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assert vlearn.batch_size == batch_size
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assert vlearn.gamma == gamma
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assert vlearn.mean_bound == mean_bound
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assert vlearn.cov_bound == cov_bound
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def test_vlearn_predict(simple_env):
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vlearn = VLEARN("CartPole-v1")
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obs, _ = simple_env.reset()
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action, _ = vlearn.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_vlearn_learn():
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vlearn = VLEARN("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, _ = vlearn.predict(obs)
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next_obs, reward, done, truncated, _ = env.step(action)
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vlearn.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 = vlearn.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_vlearn_training(simple_env):
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vlearn = VLEARN("CartPole-v1", total_timesteps=10000)
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initial_performance = evaluate_policy(vlearn, simple_env)
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vlearn.train()
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final_performance = evaluate_policy(vlearn, simple_env)
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assert final_performance > initial_performance, "VLearn 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
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