Reimplementation of Soft Actor-Critic Algorithms and Applications and a deterministic variant of SAC from Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.
Changed mujoco environments to pybullet since I found it bothering to install mujoco. Deleted command line options.(Change arguments manually) Deleted tensorboard options. Deleted Deterministic Policy model.
usage: python main.py
set args in main.py.
policy POLICY Policy Type: Gaussian | Deterministic (default:
Gaussian)
eval EVAL Evaluates a policy a policy every 10 episode (default:
True)
gamma G discount factor for reward (default: 0.99)
tau G target smoothing coefficient(τ) (default: 5e-3)
lr G learning rate (default: 3e-4)
alpha G Temperature parameter α determines the relative
importance of the entropy term against the reward
(default: 0.2)
automatic_entropy_tuning G
Automaically adjust α (default: False)
seed N random seed (default: 123456)
batch_size N batch size (default: 256)
num_steps N maximum number of steps (default: 1e6)
hidden_size N hidden size (default: 256)
updates_per_step N model updates per simulator step (default: 1)
start_steps N Steps sampling random actions (default: 1e4)
target_update_interval N
Value target update per no. of updates per step
(default: 1)
replay_size N size of replay buffer (default: 1e6)
cuda run on CUDA (default: False)
| Environment (--env-name)|
| ---------------| -------------|
| InvertedPendulumBulletEnv-v0|