added HalfCheetah and Hopper hyperparams

PPO_preTrained/README.md

@@ -2,6 +2,93 @@
 
 Hyperparameters used to obtain the `preTrained` networks are listed below :
 
+
+### RoboschoolHalfCheetah-v1
+
+```
+####### initialize environment hyperparameters ######
+
+env_name = "RoboschoolHalfCheetah-v1"
+
+has_continuous_action_space = True
+
+max_ep_len = 1000                   # max timesteps in one episode
+max_training_timesteps = int(3e6)   # break training loop if timeteps > max_training_timesteps
+
+print_freq = max_ep_len * 10               # print avg reward in the interval (in num timesteps)
+log_freq = max_ep_len * 2                 # log avg reward in the interval (in num timesteps)
+save_model_freq = int(1e5)      # save model frequency (in num timesteps)
+
+action_std = 0.6                    # starting std for action distribution (Multivariate Normal)
+action_std_decay_rate = 0.05        # linearly decay action_std (action_std = action_std - action_std_decay_rate)
+min_action_std = 0.1                # minimum action_std (stop decay after action_std <= min_action_std)
+action_std_decay_freq = int(2.5e5)  # action_std decay frequency (in num timesteps)
+
+#####################################################
+
+
+## Note : print/log frequencies should be > than max_ep_len
+
+
+################ PPO hyperparameters ################
+
+update_timestep = max_ep_len * 4      # update policy every n timesteps
+K_epochs = 80               # update policy for K epochs in one PPO update
+
+eps_clip = 0.2          # clip parameter for PPO
+gamma = 0.99            # discount factor
+
+lr_actor = 0.0003       # learning rate for actor network
+lr_critic = 0.001       # learning rate for critic network
+
+random_seed = 0         # set random seed if required (0 = no random seed)
+
+#####################################################
+```
+
+### RoboschoolHopper-v1
+
+```
+####### initialize environment hyperparameters ######
+
+env_name = "RoboschoolHopper-v1"
+
+has_continuous_action_space = True
+
+max_ep_len = 1000                   # max timesteps in one episode
+max_training_timesteps = int(3e6)   # break training loop if timeteps > max_training_timesteps
+
+print_freq = max_ep_len * 10               # print avg reward in the interval (in num timesteps)
+log_freq = max_ep_len * 2                 # log avg reward in the interval (in num timesteps)
+save_model_freq = int(1e5)      # save model frequency (in num timesteps)
+
+action_std = 0.6                    # starting std for action distribution (Multivariate Normal)
+action_std_decay_rate = 0.05        # linearly decay action_std (action_std = action_std - action_std_decay_rate)
+min_action_std = 0.1                # minimum action_std (stop decay after action_std <= min_action_std)
+action_std_decay_freq = int(2.5e5)  # action_std decay frequency (in num timesteps)
+
+#####################################################
+
+
+## Note : print/log frequencies should be > than max_ep_len
+
+
+################ PPO hyperparameters ################
+
+update_timestep = max_ep_len * 4      # update policy every n timesteps
+K_epochs = 80               # update policy for K epochs in one PPO update
+
+eps_clip = 0.2          # clip parameter for PPO
+gamma = 0.99            # discount factor
+
+lr_actor = 0.0003       # learning rate for actor network
+lr_critic = 0.001       # learning rate for critic network
+
+random_seed = 0         # set random seed if required (0 = no random seed)
+
+#####################################################
+```
+
 ### RoboschoolWalker2d-v1
 
 ```