Edit README.md & main.py

README.md

@@ -36,31 +36,46 @@ python main.py --env-name Humanoid-v2 --alpha 0.05 --tau 1 --target_update_inter
 python main.py --env-name Humanoid-v2 --policy Deterministic --tau 1 --target_update_interval 1000
 ```
 
-### Default Parameters
+### Default Arguments
 -------------
 
+```
+usage: main.py [-h] [--env-name ENV_NAME] [--policy POLICY] [--eval EVAL]
+               [--gamma G] [--tau G] [--lr G] [--alpha G]
+               [--automatic_entropy_tuning G] [--seed N] [--batch_size N]
+               [--num_steps N] [--hidden_size N] [--updates_per_step N]
+               [--start_steps N] [--target_update_interval N]
+               [--replay_size N] [--cuda]
 
-| Parameters     | Value  |
-| --------------- | ------------- |
-|**Shared**|-|
-| optimizer | Adam |
-| learning rate(`--lr`)  | 3x10<sup>−4</sup> |
-| discount(`--gamma`) (γ) | 0.99 |
-| replay buffer size(`--replay_size`) | 1x10<sup>6</sup> |
-| automatic_entropy_tuning(`--automatic_entropy_tuning`)|False|
-|number of hidden layers (all networks)|2|
-|number of hidden units per layer(`--hidden_size`)|256|
-|number of samples per minibatch(`--batch_size`)|256|
-|nonlinearity|ReLU|
-|**SAC**|-|
-|target smoothing coefficient(`--tau`) (τ)|0.005|
-|target update interval(`--target_update_interval`)|1|
-|gradient steps(`--updates_per_step`)|1|
-|**SAC** *(Hard Update)*|-|
-|target smoothing coefficient(`--tau`) (τ)|1|
-|target update interval(`--target_update_interval`)|1000|
-|gradient steps (except humanoids)(`--updates_per_step`)|4|
-|gradient steps (humanoids)(`--updates_per_step`)|1|
+PyTorch Soft Actor-Critic Args
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --env-name ENV_NAME   Mujoco Gym environment (default: HalfCheetah-v2)
+  --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: 0.005)
+  --lr G                learning rate (default: 3x10<sup>-4</sup>)
+  --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: 10<sup>6</sup>)
+  --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: 10<sup>4</sup>)
+  --target_update_interval N
+                        Value target update per no. of updates per step
+                        (default: 1)
+  --replay_size N       size of replay buffer (default: 10<sup>6</sup>)
+  --cuda                run on CUDA (default: False)
+```
 
 ------------
 

main.py

@@ -8,13 +8,13 @@ from sac import SAC
 from tensorboardX import SummaryWriter
 from replay_memory import ReplayMemory
 
-parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')
+parser = argparse.ArgumentParser(description='PyTorch Soft Actor-Critic Args')
 parser.add_argument('--env-name', default="HalfCheetah-v2",
-                    help='name of the environment to run')
+                    help='Mujoco Gym environment (default: HalfCheetah-v2)')
 parser.add_argument('--policy', default="Gaussian",
-                    help='algorithm to use: Gaussian | Deterministic')
+                    help='Policy Type: Gaussian | Deterministic (default: Gaussian)')
 parser.add_argument('--eval', type=bool, default=True,
-                    help='Evaluates a policy a policy every 10 episode (default:True)')
+                    help='Evaluates a policy a policy every 10 episode (default: True)')
 parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                     help='discount factor for reward (default: 0.99)')
 parser.add_argument('--tau', type=float, default=0.005, metavar='G',
@@ -22,11 +22,12 @@ parser.add_argument('--tau', type=float, default=0.005, metavar='G',
 parser.add_argument('--lr', type=float, default=0.0003, metavar='G',
                     help='learning rate (default: 0.0003)')
 parser.add_argument('--alpha', type=float, default=0.2, metavar='G',
-                    help='Temperature parameter α determines the relative importance of the entropy term against the reward (default: 0.2)')
+                    help='Temperature parameter α determines the relative importance of the entropy\
+                            term against the reward (default: 0.2)')
 parser.add_argument('--automatic_entropy_tuning', type=bool, default=False, metavar='G',
-                    help='Temperature parameter α automaically adjusted.')
-parser.add_argument('--seed', type=int, default=456, metavar='N',
-                    help='random seed (default: 456)')
+                    help='Automaically adjust α (default: False)')
+parser.add_argument('--seed', type=int, default=123456, metavar='N',
+                    help='random seed (default: 123456)')
 parser.add_argument('--batch_size', type=int, default=256, metavar='N',
                     help='batch size (default: 256)')
 parser.add_argument('--num_steps', type=int, default=1000001, metavar='N',