from utils import serialize_floats
from stable_baselines.deepq.replay_buffer import ReplayBuffer
from envs.state import State
store_id = get_store_id(cfg.vals["train_data"])
TIME_STEPS = cfg.vals["episode_len"]
prior = Prior(config=cfg.vals)
env = AllocationEnv(config=cfg.vals, prior=prior, load_model=True)
results = {'rewards': [0.0]}
buffer = ReplayBuffer(size=50000)
obs = env.reset()
for i in range(TIME_STEPS):
action = env.action_space.sample()
proposed_action = AllocationEnv.check_action(obs['board_config'], action)
new_obs, rew, dones, info = env.step(proposed_action)
if rew == -1:
action = 0
print("Timestep: {}".format(i))
print("action: {} - reward: {}".format(action, rew))
print(obs['day_vec'])
print(obs['board_config'])
results['rewards'].append(rew + results['rewards'][-1])
# add (s, a, r, s') to buffer
buffer.add(obs_t=State.get_vec_observation(obs),
action=action,
reward=rew,
results = {'rewards': [0.0]}
buffer = ReplayBuffer(size=50000)
for j in range(100):
obs = env.reset()
for i in range(TEST_T):
feasible_actions = AllocationEnv.get_feasible_actions(
obs["board_config"])
action_mask = AllocationEnv.get_action_mask(feasible_actions,
n_actions)
action, _states = model.predict(obs, mask=action_mask)
action = AllocationEnv.check_action(obs['board_config'], action)
new_obs, r, dones, info = env.step([action])
results['rewards'].append(r[0] + results['rewards'][-1])
# add (s, a, r, s') to buffer
buffer.add(obs_t=State.get_vec_observation(obs),
action=action,
reward=r[0],
obs_tp1=State.get_vec_observation(new_obs),
done=float(dones))
obs = new_obs
with open("output/rl-test-{}.json".format(cfg.vals['prj_name']), 'w') as f:
json.dump(results, f)
from envs.allocation_env import AllocationEnv
import config.config as cfg
import matplotlib.pyplot as plt
from utils import serialize_floats
import json
TIME_STEPS = cfg.vals["episode_len"]
prior = Prior(config=cfg.vals)
env = AllocationEnv(config=cfg.vals, prior=prior, load_model=True)
results = {'rewards': [0.0]}
obs = env.reset()
for i in range(TIME_STEPS):
action = 0
action = AllocationEnv.check_action(obs['board_config'], action)
obs, rew, dones, info = env.step(action)
print("Timestep: {}".format(i))
print("action: {} - reward: {}".format(action, rew))
print(obs['day_vec'])
print(obs['board_config'])
results['rewards'].append(rew + results['rewards'][-1])
print(results['rewards'])
x = np.arange(TIME_STEPS+1)
plt.plot(x, results['rewards'])
plt.xlabel("Timestep (t)")
plt.ylabel("Cumulative Reward")
plt.savefig("figs/naive-policy-{}.png".format(cfg.vals['prj_name']))