def build_agent(env, env_id, agent_configs):
agent_configs = {}
if (env_id in AWR_CONFIGS):
agent_configs.update(AWR_CONFIGS[env_id])
graph = tf.Graph()
sess = tf.Session(graph=graph)
agent = awr_agent.AWRAgent(env=env, sess=sess, **agent_configs)
return agent
def build_agent(env):
env_id = arg_parser.env
agent_configs = {}
if (env_id in awr_configs.AWR_CONFIGS):
agent_configs = awr_configs.AWR_CONFIGS[env_id]
graph = tf.Graph()
sess = tf.Session(graph=graph)
agent = awr_agent.AWRAgent(env=env, sess=sess, **agent_configs)
return agent
.format(data_std, data_std), "rb")), args.constraint)
terminals = []
for el in datas:
terminal = np.ones(len(el[0]))
terminal[-1] = 0
terminals.append(terminal)
datas2 = []
for el, terminal in zip(datas, terminals):
datas2.append((el[0], el[1], el[2], terminal))
env = gym.make("Reacher-v2")
graph = tf.Graph()
sess = tf.Session(graph=graph)
agent = awr_agent.AWRAgent(env=env, sess=sess, **configs)
agent.load_model(
"../output/Reacher-v2_{}_offline/model.ckpt".format(data_std))
qnn = Q(agent.get_state_size(), agent.get_action_size(), 0.001).cuda()
eval_std = args.eval_std # For each, try different eval_std
num_epochs = args.n_epochs
FQE = FittedQEvaluation(qnn)
policy = Policy(agent, eval_std)
FQE.fit_Q(policy, datas2, num_epochs, agent._discount)
vals0 = []
for _ in tqdm(range(100)):
path = rollout_path(agent, eval_std)
