Remove reference to actor and critic from DDPG

reinforced_lib/agents/deep/ddpg.py

@@ -22,19 +22,19 @@ class DDPGState(AgentState):
  Attributes
  ----------
  q_params : dict
- Parameters of the Q-network (critic).
+ Parameters of the Q-network.
  q_net_state : dict
- State of the Q-network (critic).
+ State of the Q-network.
  q_params_target : dict
  Parameters of the target Q-network.
  q_net_state_target : dict
  State of the target Q-network.
  q_opt_state : optax.OptState
  Optimizer state of the Q-network.
  a_params : dict
- Parameters of the policy network (actor).
+ Parameters of the policy network.
  a_net_state : dict
- State of the policy network (actor).
+ State of the policy network.
  a_params_target : dict
  Parameters of the target policy network.
  a_net_state_target : dict
@@ -71,17 +71,17 @@ class DDPG(BaseAgent):
  Deep deterministic policy gradient [3]_ [4]_ agent with white Gaussian noise exploration and experience replay
  buffer. The agent simultaneously learns a Q-function and a policy. The Q-function is updated using the Bellman
  equation. The policy is learned using the gradient of the Q-function with respect to the policy parameters
- to maximize the Q-value. The agent uses two Q-networks (critics) and two policy networks (actors) to stabilize
- the learning process and avoid overestimation. The target networks are updated with a soft update. This agent
- follows the off-policy learning paradigm and is suitable for environments with continuous action spaces.
+ to maximize the Q-value. The agent uses two Q-networks and two policy networks to stabilize the learning process
+ and avoid overestimation. The target networks are updated with a soft update. This agent follows the off-policy
+ learning paradigm and is suitable for environments with continuous action spaces.
 
  Parameters
  ----------
  q_network : nn.Module
- Architecture of the Q-networks (critics).
+ Architecture of the Q-networks.
  The input to the network should be two tensors of observations and actions respectively.
  a_network : nn.Module
- Architecture of the policy networks (actors).
+ Architecture of the policy networks.
  obs_space_shape : Shape
  Shape of the observation space.
  act_space_shape : Shape