def rollout(env: gym.Env,
policies: policies.QPolicy,
episodes: int,
temp: float,
render: bool = False):
"""
Simulates trajectories for the given number of episodes. Input policy is used to sample actions at each time step
:param env: the gym environment
:param policies: The policy used to sample actions (Tabular/DQN)
:param episodes: Number of episodes to be simulated
:param epsilon: The exploration parameter for epsilon-greedy policy
:param gamma: Discount factor
:param render: If True, render the environment
:return replay: Collection of (state, action, reward, next_state, done) at each timestep of all simulated episodes
:return scores: Collection of total reward for each simulated episode
"""
replay = []
scores = []
for itrnum in range(episodes):
state = env.reset()
step = 0
score = 0
done = False
while not done:
if render:
env.render()
pi = policies(state, temp)
# How do you select the action given pi. Hint: use np.random.choice
#print("SIZE STATE:", np.size(state), "SIZE PI:", np.size(pi), "")
action = np.random.choice([0, 1, 2],
p=pi) #TODO: fill in this line
#raise Exception("STATE:",state,"PI:",pi,"ACTION:",action,"TYPE:",type(action))
next_state, reward, done, _ = env.step(action)
score += reward
replay.append((state, action, reward, next_state, done))
state = next_state
step += 1
env.close()
scores.append(score)
return replay, scores
def rollout(env: gym.Env, policies: policies.QPolicy, episodes: int, epsilon: float, render: bool = False):
"""
Simulates trajectories for the given number of episodes. Input policy is used to sample actions at each time step
:param env: the gym environment
:param policies: The policy used to sample actions (Tabular/DQN)
:param episodes: Number of episodes to be simulated
:param epsilon: The exploration parameter for epsilon-greedy policy
:param gamma: Discount factor
:param render: If True, render the environment
:return replay: Collection of (state, action, reward, next_state, done) at each timestep of all simulated episodes
:return scores: Collection of total reward for each simulated episode
"""
replay = []
scores = []
for _ in range(episodes):
state = env.reset()
step = 0
score = 0
done = False
while not done:
if render:
env.render()
### <<< Your Code Here
# print(state,episodes)
pi = policies(state, epsilon)
# How do you select the action given pi. Hint: use np.random.choice
if pi[0] == pi[1]:
action = np.random.choice(2)
# print(action, 'random')
else:
action = np.argmax(pi)
# print(action, 'not random')
### Your Code Ends >>>
next_state, reward, done, _ = env.step(action)
score += reward
replay.append((state, action, reward, next_state, done))
state = next_state
step += 1
env.close()
scores.append(score)
return replay, scores
def rollout(env: gym.Env,
policies: policies.QPolicy,
episodes: int,
temp: float,
render: bool = False):
"""
Simulates trajectories for the given number of episodes. Input policy is used to sample actions at each time step
:param env: the gym environment
:param policies: The policy used to sample actions (Tabular/DQN)
:param episodes: Number of episodes to be simulated
:param epsilon: The exploration parameter for epsilon-greedy policy
:param gamma: Discount factor
:param render: If True, render the environment
:return replay: Collection of (state, action, reward, next_state, done) at each timestep of all simulated episodes
:return scores: Collection of total reward for each simulated episode
"""
replay = []
scores = []
for itrnum in range(episodes):
state = env.reset()
step = 0
score = 0
done = False
while not done:
if render:
env.render()
pi = policies(state, temp)
# How do you select the action given pi. Hint: use np.random.choice
# action = np.random.choice(a=[0, 1, 2], size=1, p=pi)[0] # for tabQ learning
action = np.random.choice(a=[0, 1], size=1,
p=pi)[0] # for DQN learning
next_state, reward, done, _ = env.step(action)
score += reward
replay.append((state, action, reward, next_state, done))
state = next_state
step += 1
env.close()
scores.append(score)
return replay, scores
