def train(discount, n_trajectories, epochs, learning_rate): """ Run maximum entropy inverse reinforcement learning on the gridworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. n_trajectories: Number of sampled trajectories. int. epochs: Gradient descent iterations. int. learning_rate: Gradient descent learning rate. float. """ trajectory_length = 276 env = Game.GameState() trajectories = env.generate_trajectories(n_trajectories, trajectory_length, env.optimal_policy_deterministic) feature_matrix = env.feature_matrix() r = maxent.irl(feature_matrix, env.n_actions, discount, env.transition_probability, trajectories, epochs, learning_rate, "flappy_alpha_%d.pkl", "flappy_alpha_96.pkl", 96) pkl.dump(r, open("flappy_maxent_reward.pkl", 'wb')) return r
def train(discount): """ Run linear programming inverse reinforcement learning on the gridworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. """ env = Game.GameState() r = linear_irl.irl(env.n_states, env.n_actions, env.transition_probability, env.get_policy(), discount, 1, 5) pkl.dump(r, open("flappy_lp_reward.pkl", 'wb'))
def main(discount, n_objects, n_colours, n_trajectories, epochs, learning_rate, structure): # n_objects, n_colours 随便给的 """ Run deep maximum entropy inverse reinforcement learning on the objectworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. n_objects: Number of objects. int. n_colours: Number of colours. int. n_trajectories: Number of sampled trajectories. int. epochs: Gradient descent iterations. int. learning_rate: Gradient descent learning rate. float. structure: Neural network structure. Tuple of hidden layer dimensions, e.g., () is no neural network (linear maximum entropy) and (3, 4) is two hidden layers with dimensions 3 and 4. """ trajectory_length = 268 l1 = l2 = 0 # env = Env(n_objects, n_colours) env=Game.GameState() # ground_r = np.array([env.reward_deep_maxent(s) for s in range(env.n_states)]) # policy = find_policy(env.n_states, env.n_actions, env.transition_probability, # ground_r, discount, stochastic=False) # trajectories = env.generate_trajectories(n_trajectories, trajectory_length, trajectories = env.generate_trajectories(n_trajectories, trajectory_length, env.optimal_policy_deterministic) # feature_matrix = env.feature_matrix_deep_maxent(discrete=False) feature_matrix = env.feature_matrix() r = deep_maxent.irl((feature_matrix.shape[1],) + structure, feature_matrix, env.n_actions, discount, env.transition_probability, trajectories, epochs, learning_rate, l1=l1, l2=l2) pkl.dump(r, open('flappy_deep_maxent_reward.pkl', 'wb'))
def train(discount, n_trajectories, epochs, learning_rate): """ Run maximum entropy inverse reinforcement learning on the gridworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. n_trajectories: Number of sampled trajectories. int. epochs: Gradient descent iterations. int. learning_rate: Gradient descent learning rate. float. """ trajectory_length = 276 env = Game.GameState() trajectories = env.generate_trajectories(n_trajectories, trajectory_length, env.optimal_policy_deterministic) def feature_function(state): feature = np.zeros(env.n_states) feature[state]=1 return feature def transitionProbability(state_code, action): res = {} for i in range(env.n_states): res[state_code]=env._transition_probability(state_code, action, i) return res irl = LargeGradientIRL(env.n_actions, env.n_states, transitionProbability, feature_function, discount, learning_rate, trajectories, epochs) result = irl.gradientIterationIRL() reward=result[-1][0].reshape(env.n_states, ) pkl.dump(result, open("lg_result.pkl", 'wb')) pkl.dump(reward, open("lg_reward.pkl", 'wb')) return reward
irl = LargeGradientIRL(env.n_actions, env.n_states, transitionProbability, feature_function, discount, learning_rate, trajectories, epochs) result = irl.gradientIterationIRL() reward=result[-1][0].reshape(env.n_states, ) pkl.dump(result, open("lg_result.pkl", 'wb')) pkl.dump(reward, open("lg_reward.pkl", 'wb')) return reward if __name__ == '__main__': with tf.device('/cpu:0'): train(0.01, 1, 400, 0.01) rewards = pkl.load(open("flappy_maxent_reward.pkl", 'rb')) env = Game.GameState(prepare_tp=True) value = vi.value(env.get_policy(), env.n_states, env.transition_probability, rewards, 0.3) opt_value = vi.optimal_value(env.n_states, env.n_actions, env.transition_probability, rewards, 0.3) pkl.dump(value, open("flappy_maxent_value.pkl", 'wb')) pkl.dump(opt_value, open("flappy_maxent_opt_value.pkl", 'wb')) value=pkl.load(open("flappy_maxent_value.pkl", 'rb')) opt_value=pkl.load(open("flappy_maxent_opt_value.pkl", 'rb')) status = validate(value) print(status) pkl.dump(status, open("flappy_maxent_status.pkl", 'wb')) status = validate(opt_value) print(status)