exp_name: "ResNetEncoder"
#the name of this experiment#
seed: 1
#seed of the experiment#
torch_deterministic: true
#if toggled, `torch.backends.cudnn.deterministic=False`#
device: "cuda"
#if toggled, cuda will be enabled by default#rue
track: false
#if toggled, this experiment will be tracked with Weights and Biases#


# Algorithm specific arguments
total_timesteps: 20
#total timesteps of the experiments#
buffer_size: 40000
#the replay memory buffer size#
gamma: 0
#the discount factor gamma#
tau: 0.005
#target smoothing coefficient (default: 0.005)#
batch_size: 64
#the batch size of sample from the reply memory#
learning_starts: 10
#timestep to start learning#
policy_lr: 0.0003
#the learning rate of the policy network optimizer#
q_lr: 0.0003
#the learning rate of the Q network network optimizer#
policy_frequency: 2
#the frequency of training policy (delayed)#
target_network_frequency: 1  # Denis Yarats' implementation delays this by 2.
#the frequency of updates for the target nerworks#
alpha: 0.2
#Entropy regularization coefficient.#
autotune: true
#automatic tuning of the entropy coefficient#.
max_episode_timesteps: 1