Created
July 1, 2017 03:20
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CartPole-v0 Hill Climb + MC(10) + Gaussian Noise (Sigma 0.5)
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| import gym | |
| import numpy as np | |
| from gym.wrappers.monitoring import Monitor | |
| MC_POLICY_EVAL_EP = 10 | |
| BASE_NOISE_FACTOR = 0.5 | |
| NUM_POLICY_EVAL = 500 | |
| env = gym.make('CartPole-v0') | |
| env = Monitor(env, 'tmp/cart-pole-hill-climb-3', 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 | |
| print("Running Hill Climb on Cart Pole") | |
| print("Params:\n\tMC Eval Count: {0} trajectories\n\tBase Noise Factor: {1}".format( | |
| MC_POLICY_EVAL_EP, | |
| BASE_NOISE_FACTOR, | |
| )) | |
| for i_episode in range(NUM_POLICY_EVAL): | |
| # Weights are 1x4 matrix | |
| # µ = 0 , sigma 1 | |
| annealing_term = 1 - (i_episode / NUM_POLICY_EVAL) | |
| noise_scaling = BASE_NOISE_FACTOR * annealing_term | |
| print("Applying jitter with factor {0} to parameters {1}".format( | |
| noise_scaling, | |
| best_params, | |
| )) | |
| # Add gaussian noise | |
| # µ = 0 , sigma = noise_scaling | |
| noise_term = np.random.randn(4) * noise_scaling | |
| parameters = best_params + noise_term | |
| episodic_reward = evaluate_policy(MC_POLICY_EVAL_EP, 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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