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_Memory::C_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 |
Crlpack.dqn.dqn.Dqn | This is a helper class that selects the correct the variant of DQN implementations based on prioritization strategy determined by the argument prioritization_params |
Crlpack.environments.environments.Environments | This class is a generic class to train any agent in any environment |
Crlpack._C.grad_accumulator.GradAccumulator | This class provides the python interface to C_GradAccumulator, the C++ class which performs heavier workloads |
▼Crlpack.utils.base.internal_code_register.InternalCodeRegister | |
Crlpack.utils.internal_code_setup.InternalCodeSetup | |
Crlpack.utils.setup.Setup | This class sets up all the necessary objects that are required to run any configuration |
▼Cpytorch.nn.Module | |
Crlpack.models._mlp_feature_extractor._MlpFeatureExtractor | This class is a PyTorch Model implementing the MLP based feature extractor for 1-D or 2-D state values |
Crlpack.models.actor_critic_mlp_policy.ActorCriticMlpPolicy | This class is a PyTorch Model implementing the MLP based Actor-Critic Policy |
Crlpack.models.mlp.Mlp | This class is a PyTorch Model implementing the MLP model for 1-D or 2-D state values |
Crlpack.utils.normalization.Normalization | Normalization class providing methods for normalization techniques |
▼Cobject | |
Crlpack._C.memory.Memory | This class provides the python interface to C_Memory, the C++ class which performs heavier workloads |
▼Crlpack.utils.base.agent.Agent | The base class for all agents |
▼Crlpack.actor_critic.a2c.A2C | The A2C class implements the synchronous Actor-Critic method |
Crlpack.actor_critic.a3c.A3C | The A2C class implements the synchronous Actor-Critic method |
▼Crlpack.dqn.dqn_agent.DqnAgent | This class implements the basic DQN methodology, i.e |
Crlpack.dqn.dqn_proportional_prioritization_agent.DqnProportionalPrioritizationAgent | This class implements the DQN with Proportional prioritization strategy |
Crlpack.dqn.dqn_rank_based_prioritization_agent.DqnRankBasedPrioritizationAgent | This class implements the DQN with Rank-Based prioritization strategy |
COffload< DType > | Template Offload class for CPU with CPU optimized OpenMP routines |
COffload< float_t > | |
COffload< int64_t > | |
▼Crlpack.utils.base.register.Register | This abstract class contains all the necessary information about agents and models for setting them up |
Crlpack.utils.sanity_check.SanityCheck | This class does the basic sanity check of input_config |
Crlpack.utils.setup.Setup | This class sets up all the necessary objects that are required to run any configuration |
Crlpack.simulator.Simulator | Simulator class simulates the environments and runs the agent through the environment |
Crlpack.simulator_distributed.SimulatorDistributed | Similar to rlpack.simulator.Simulator, SimulatorDistributed class sets up agents and runs simulation by interacting with the given environment |
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 |