Created
July 1, 2017 02:34
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CartPole-v0 Hill Climb with MC(10) Eval + Simulated Annealing
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| import gym | |
| import numpy as np | |
| from gym.wrappers.monitoring import Monitor | |
| env = gym.make('CartPole-v0') | |
| env = Monitor(env, 'tmp/cart-pole-hill-climb-2', force=True) | |
| print("Action space: {0}".format(env.action_space)) | |
| print("Observation space: {0}\n\tLow: {1}\n\tHigh: {2}".format( | |
| env.observation_space, | |
| env.observation_space.low, | |
| env.observation_space.high, | |
| )) | |
| def action_selection(weights, observation): | |
| if np.matmul(weights, observation) < 0: | |
| return 0 | |
| else: | |
| return 1 | |
| def run_episode(weights): | |
| observation = env.reset() | |
| total_reward = 0 | |
| for t in range(200): | |
| env.render() | |
| action = action_selection(weights, observation) | |
| observation, reward, done, info = env.step(action) | |
| total_reward += reward | |
| if done: | |
| print("Episode finished after {0} timesteps with reward {1}".format( | |
| t + 1, | |
| total_reward, | |
| )) | |
| break | |
| return total_reward | |
| def evaluate_policy(num_episodes, weights): | |
| mean_reward = 0 | |
| for k in range(1, num_episodes + 1): | |
| reward = run_episode(weights) | |
| error = reward - mean_reward | |
| mean_reward += error / k | |
| print("Mean reward estimated as {0} for past {1} episodes".format( | |
| mean_reward, | |
| num_episodes | |
| )) | |
| return mean_reward | |
| best_reward = -np.inf | |
| best_params = np.random.rand(4) * 2 - 1 | |
| num_eval_eps = 10 | |
| base_noise_factor = 1. | |
| num_evaluations = 1000 | |
| print("Running Hill Climb on Cart Pole") | |
| print("Params:\n\tMC Eval Count: {0} trajectories\n\tBase Noise Factor: {1}".format( | |
| num_eval_eps, | |
| base_noise_factor, | |
| )) | |
| for i_episode in range(num_evaluations): | |
| # Weights are 1x4 matrix | |
| # µ = 0 , sigma 1 | |
| noise_scaling = 1 - (i_episode / num_evaluations) | |
| print("Applying jitter with factor {0} to parameters {1}".format( | |
| noise_scaling, | |
| best_params, | |
| )) | |
| noise_term = (np.random.rand(4) * 2 - 1) * noise_scaling | |
| parameters = best_params + noise_term | |
| episodic_reward = evaluate_policy(num_eval_eps, parameters) | |
| if episodic_reward > best_reward: | |
| print("Episode {2}: Got new best reward of {0}, better than previous of {1}".format( | |
| episodic_reward, | |
| best_reward, | |
| i_episode, | |
| )) | |
| best_reward = episodic_reward | |
| best_params = parameters | |
| env.close() |
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