▼Nrlpack | Implementation of RL Algorithms built on top of PyTorch |
▼N_C | This package implements the classes to interface between C++ and Python |
▼Ngrad_accumulator | |
CGradAccumulator | This class provides the python interface to C_GradAccumulator, the C++ class which performs heavier workloads |
▼Nmemory | |
CMemory | This class provides the python interface to C_Memory, the C++ class which performs heavier workloads |
▼Nactor_critic | This package implements the Actor-Critic methods |
▼Na2c | |
CA2C | The A2C class implements the synchronous Actor-Critic method |
▼Na3c | |
CA3C | The A2C class implements the synchronous Actor-Critic method |
▼Ndqn | This package implements the DQN methods |
▼Ndqn | |
CDqn | This is a helper class that selects the correct the variant of DQN implementations based on prioritization strategy determined by the argument prioritization_params |
▼Ndqn_agent | |
CDqnAgent | This class implements the basic DQN methodology, i.e |
▼Ndqn_proportional_prioritization_agent | |
CDqnProportionalPrioritizationAgent | This class implements the DQN with Proportional prioritization strategy |
▼Ndqn_rank_based_prioritization_agent | |
CDqnRankBasedPrioritizationAgent | This class implements the DQN with Rank-Based prioritization strategy |
▼Nenvironments | This package implements the gym environment to couple it with selected environment |
▼Nenvironments | |
CEnvironments | This class is a generic class to train any agent in any environment |
▼Nmodels | This package implements the in-built models |
▼N_mlp_feature_extractor | |
C_MlpFeatureExtractor | This class is a PyTorch Model implementing the MLP based feature extractor for 1-D or 2-D state values |
▼Nactor_critic_mlp_policy | |
CActorCriticMlpPolicy | This class is a PyTorch Model implementing the MLP based Actor-Critic Policy |
▼Nmlp | |
CMlp | This class is a PyTorch Model implementing the MLP model for 1-D or 2-D state values |
▼Nsimulator | |
CSimulator | Simulator class simulates the environments and runs the agent through the environment |
▼Nsimulator_distributed | |
CSimulatorDistributed | Similar to rlpack.simulator.Simulator, SimulatorDistributed class sets up agents and runs simulation by interacting with the given environment |
▼Nutils | This package implements the basic utilities to be used across rlpack |
▼Nbase | This package implements the base classes to be used across rlpack |
▼Nagent | |
CAgent | The base class for all agents |
▼Ninternal_code_register | |
CInternalCodeRegister | |
▼Nregister | |
CRegister | This abstract class contains all the necessary information about agents and models for setting them up |
▼Ninternal_code_setup | |
CInternalCodeSetup | |
▼Nnormalization | |
CNormalization | Normalization class providing methods for normalization techniques |
▼Nsanity_check | |
CSanityCheck | This class does the basic sanity check of input_config |
▼Nsetup | |
CSetup | This class sets up all the necessary objects that are required to run any configuration |
CC_GradAccumulator | |
▼CC_Memory | The class C_Memory is the C++ backend for memory-buffer used in algorithms that stores transitions in a buffer. This class contains optimized routines to support Python front-end of rlpack._C.memory.Memory class |
CC_MemoryData | The class C_MemoryData keeps the references to data that is associated with C_Memory. This class implements the functions necessary to retrieve the data by de-referencing the data associated with C_Memory |
COffload | Template Offload class for CPU with CPU optimized OpenMP routines |
CSumTree | The class SumTree is a class which represents the Sum-Tree which is used in proportional prioritization. It implements all the methods necessary to create the Sum-Tree and sample from it |
CSumTreeNode | The class SumTreeNode is a private class which represents a node in Sum-Tree. This is only used when we use proportional prioritization |