git clone https://github.com/mimoralea/gym-walk.git
cd gym-walk
pip install .or:
pip install git+https://github.com/mimoralea/gym-walk#egg=gym-walkimport gym, gym_walk, numpy as np
env = gym.make('WalkFive-v0')
pi = lambda x: np.random.randint(2)
def td(pi, env, gamma=1.0, alpha=0.01, n_episodes=100000):
V = np.zeros(env.observation_space.n)
for t in range(n_episodes):
state, done = env.reset(), False
while not done:
action = pi(state)
next_state, reward, done, _ = env.step(action)
td_target = reward + gamma * V[next_state] * (not done)
td_error = td_target - V[state]
V[state] = V[state] + alpha * td_error
state = next_state
return V
V = td(pi, env)
V
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