def _define_simulated_batch_env(hparams, num_agents, simulation_random_starts=False, intrinsic_reward_scale=0.): cur_batch_env = simulated_batch_env.SimulatedBatchEnv( hparams, num_agents, simulation_random_starts, intrinsic_reward_scale) return cur_batch_env
def define_simulated_batch_env(num_agents): # TODO(blazej0): the parameters should be infered. observ_shape = (210, 160, 3) observ_dtype = tf.float32 action_shape = [] action_dtype = tf.int32 cur_batch_env = simulated_batch_env.SimulatedBatchEnv( num_agents, observ_shape, observ_dtype, action_shape, action_dtype) return cur_batch_env
def define_simulated_batch_env(environment_lambda, num_agents, problem, simulation_random_starts=False, intrinsic_reward_scale=0.): cur_batch_env = simulated_batch_env.SimulatedBatchEnv( environment_lambda, num_agents, problem, simulation_random_starts, intrinsic_reward_scale) return cur_batch_env
def define_simulated_batch_env(num_agents): #TODO: pm->Błażej. Should the paramters be infered. observ_shape, observ_dtype, action_shape, action_dtype = ( 210, 160, 3), tf.float32, [], tf.int32 batch_env = simulated_batch_env.SimulatedBatchEnv(num_agents, observ_shape, observ_dtype, action_shape, action_dtype) return batch_env
def define_simulated_batch_env(environment_lambda, num_agents, problem): cur_batch_env = simulated_batch_env.SimulatedBatchEnv( environment_lambda, num_agents, problem) return cur_batch_env
def _define_simulated_batch_env(environment_spec, num_agents, initial_frame_chooser): cur_batch_env = simulated_batch_env.SimulatedBatchEnv( environment_spec, num_agents, initial_frame_chooser) return cur_batch_env
def _define_simulated_batch_env(environment_spec, num_agents): cur_batch_env = simulated_batch_env.SimulatedBatchEnv(environment_spec, num_agents) return cur_batch_env